+ 
+ Comparison with popular methods in terms of latency-accuracy (left) and FLOPs-accuracy (right) trade-offs
+
+
+This example demonstrates how to perform inference using YOLOv8 in C++ with ONNX Runtime and OpenCV's API.
+
+## Benefits ✨
+
+- Friendly for deployment in the industrial sector.
+- Faster than OpenCV's DNN inference on both CPU and GPU.
+- Supports FP32 and FP16 CUDA acceleration.
+
+## Note ☕
+
+1. Benefit for Ultralytics' latest release, a `Transpose` op is added to the YOLOv8 model, while make v8 and v5 has the same output shape. Therefore, you can run inference with YOLOv5/v7/v8 via this project.
+
+## Exporting YOLOv8 Models 📦
+
+To export YOLOv8 models, use the following Python script:
+
+```python
+from ultralytics import YOLO
+
+# Load a YOLOv8 model
+model = YOLO("yolov8n.pt")
+
+# Export the model
+model.export(format="onnx", opset=12, simplify=True, dynamic=False, imgsz=640)
+```
+
+Alternatively, you can use the following command for exporting the model in the terminal
+
+```bash
+yolo export model=yolov8n.pt opset=12 simplify=True dynamic=False format=onnx imgsz=640,640
+```
+
+## Exporting YOLOv8 FP16 Models 📦
+
+```python
+import onnx
+from onnxconverter_common import float16
+
+model = onnx.load(R"YOUR_ONNX_PATH")
+model_fp16 = float16.convert_float_to_float16(model)
+onnx.save(model_fp16, R"YOUR_FP16_ONNX_PATH")
+```
+
+## Download COCO.yaml file 📂
+
+In order to run example, you also need to download coco.yaml. You can download the file manually from [here](https://raw.githubusercontent.com/ultralytics/ultralytics/main/ultralytics/cfg/datasets/coco.yaml)
+
+## Dependencies ⚙️
+
+| Dependency | Version |
+| -------------------------------- | ------------- |
+| Onnxruntime(linux,windows,macos) | >=1.14.1 |
+| OpenCV | >=4.0.0 |
+| C++ Standard | >=17 |
+| Cmake | >=3.5 |
+| Cuda (Optional) | >=11.4 \<12.0 |
+| cuDNN (Cuda required) | =8 |
+
+Note: The dependency on C++17 is due to the usage of the C++17 filesystem feature.
+
+Note (2): Due to ONNX Runtime, we need to use CUDA 11 and cuDNN 8. Keep in mind that this requirement might change in the future.
+
+## Build 🛠️
+
+1. Clone the repository to your local machine.
+
+2. Navigate to the root directory of the repository.
+
+3. Create a build directory and navigate to it:
+
+ ```console
+ mkdir build && cd build
+ ```
+
+4. Run CMake to generate the build files:
+
+ ```console
+ cmake ..
+ ```
+
+ **Notice**:
+
+ If you encounter an error indicating that the `ONNXRUNTIME_ROOT` variable is not set correctly, you can resolve this by building the project using the appropriate command tailored to your system.
+
+ ```console
+ # compiled in a win32 system
+ cmake -D WIN32=TRUE ..
+ # compiled in a linux system
+ cmake -D LINUX=TRUE ..
+ # compiled in an apple system
+ cmake -D APPLE=TRUE ..
+ ```
+
+5. Build the project:
+
+ ```console
+ make
+ ```
+
+6. The built executable should now be located in the `build` directory.
+
+## Usage 🚀
+
+```c++
+//change your param as you like
+//Pay attention to your device and the onnx model type(fp32 or fp16)
+DL_INIT_PARAM params;
+params.rectConfidenceThreshold = 0.1;
+params.iouThreshold = 0.5;
+params.modelPath = "yolov8n.onnx";
+params.imgSize = { 640, 640 };
+params.cudaEnable = true;
+params.modelType = YOLO_DETECT_V8;
+yoloDetector->CreateSession(params);
+Detector(yoloDetector);
+```
diff --git a/examples/YOLOv8-ONNXRuntime-CPP/inference.cpp b/examples/YOLOv8-ONNXRuntime-CPP/inference.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..a65391f5d7d116befac6f1cb2151891cca5a1c5d
--- /dev/null
+++ b/examples/YOLOv8-ONNXRuntime-CPP/inference.cpp
@@ -0,0 +1,375 @@
+#include "inference.h"
+#include
+
+
+
+
+## Advanced Usage
+
+For more advanced usage, including real-time video processing, please refer to the `main.py` script's command-line arguments.
+
+## Contributing
+
+We welcome contributions to improve this demo! Please submit issues and pull requests for bug reports, feature requests, or submitting a new algorithm enhancement.
+
+## License
+
+This project is licensed under the AGPL-3.0 License - see the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for details.
+
+## Acknowledgments
+
+- The YOLOv8-Segmentation-ONNXRuntime-Python demo is contributed by GitHub user [jamjamjon](https://github.com/jamjamjon).
+- Thanks to the ONNX Runtime community for providing a robust and efficient inference engine.
diff --git a/examples/YOLOv8-Segmentation-ONNXRuntime-Python/main.py b/examples/YOLOv8-Segmentation-ONNXRuntime-Python/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8e2e7d55d80ba1a60d37ffb1dca7b86216ba90e
--- /dev/null
+++ b/examples/YOLOv8-Segmentation-ONNXRuntime-Python/main.py
@@ -0,0 +1,338 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import argparse
+
+import cv2
+import numpy as np
+import onnxruntime as ort
+
+from ultralytics.utils import ASSETS, yaml_load
+from ultralytics.utils.checks import check_yaml
+from ultralytics.utils.plotting import Colors
+
+
+class YOLOv8Seg:
+ """YOLOv8 segmentation model."""
+
+ def __init__(self, onnx_model):
+ """
+ Initialization.
+
+ Args:
+ onnx_model (str): Path to the ONNX model.
+ """
+ # Build Ort session
+ self.session = ort.InferenceSession(
+ onnx_model,
+ providers=["CUDAExecutionProvider", "CPUExecutionProvider"]
+ if ort.get_device() == "GPU"
+ else ["CPUExecutionProvider"],
+ )
+
+ # Numpy dtype: support both FP32 and FP16 onnx model
+ self.ndtype = np.half if self.session.get_inputs()[0].type == "tensor(float16)" else np.single
+
+ # Get model width and height(YOLOv8-seg only has one input)
+ self.model_height, self.model_width = [x.shape for x in self.session.get_inputs()][0][-2:]
+
+ # Load COCO class names
+ self.classes = yaml_load(check_yaml("coco8.yaml"))["names"]
+
+ # Create color palette
+ self.color_palette = Colors()
+
+ def __call__(self, im0, conf_threshold=0.4, iou_threshold=0.45, nm=32):
+ """
+ The whole pipeline: pre-process -> inference -> post-process.
+
+ Args:
+ im0 (Numpy.ndarray): original input image.
+ conf_threshold (float): confidence threshold for filtering predictions.
+ iou_threshold (float): iou threshold for NMS.
+ nm (int): the number of masks.
+
+ Returns:
+ boxes (List): list of bounding boxes.
+ segments (List): list of segments.
+ masks (np.ndarray): [N, H, W], output masks.
+ """
+ # Pre-process
+ im, ratio, (pad_w, pad_h) = self.preprocess(im0)
+
+ # Ort inference
+ preds = self.session.run(None, {self.session.get_inputs()[0].name: im})
+
+ # Post-process
+ boxes, segments, masks = self.postprocess(
+ preds,
+ im0=im0,
+ ratio=ratio,
+ pad_w=pad_w,
+ pad_h=pad_h,
+ conf_threshold=conf_threshold,
+ iou_threshold=iou_threshold,
+ nm=nm,
+ )
+ return boxes, segments, masks
+
+ def preprocess(self, img):
+ """
+ Pre-processes the input image.
+
+ Args:
+ img (Numpy.ndarray): image about to be processed.
+
+ Returns:
+ img_process (Numpy.ndarray): image preprocessed for inference.
+ ratio (tuple): width, height ratios in letterbox.
+ pad_w (float): width padding in letterbox.
+ pad_h (float): height padding in letterbox.
+ """
+ # Resize and pad input image using letterbox() (Borrowed from Ultralytics)
+ shape = img.shape[:2] # original image shape
+ new_shape = (self.model_height, self.model_width)
+ r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+ ratio = r, r
+ new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+ pad_w, pad_h = (new_shape[1] - new_unpad[0]) / 2, (new_shape[0] - new_unpad[1]) / 2 # wh padding
+ if shape[::-1] != new_unpad: # resize
+ img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
+ top, bottom = int(round(pad_h - 0.1)), int(round(pad_h + 0.1))
+ left, right = int(round(pad_w - 0.1)), int(round(pad_w + 0.1))
+ img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114))
+
+ # Transforms: HWC to CHW -> BGR to RGB -> div(255) -> contiguous -> add axis(optional)
+ img = np.ascontiguousarray(np.einsum("HWC->CHW", img)[::-1], dtype=self.ndtype) / 255.0
+ img_process = img[None] if len(img.shape) == 3 else img
+ return img_process, ratio, (pad_w, pad_h)
+
+ def postprocess(self, preds, im0, ratio, pad_w, pad_h, conf_threshold, iou_threshold, nm=32):
+ """
+ Post-process the prediction.
+
+ Args:
+ preds (Numpy.ndarray): predictions come from ort.session.run().
+ im0 (Numpy.ndarray): [h, w, c] original input image.
+ ratio (tuple): width, height ratios in letterbox.
+ pad_w (float): width padding in letterbox.
+ pad_h (float): height padding in letterbox.
+ conf_threshold (float): conf threshold.
+ iou_threshold (float): iou threshold.
+ nm (int): the number of masks.
+
+ Returns:
+ boxes (List): list of bounding boxes.
+ segments (List): list of segments.
+ masks (np.ndarray): [N, H, W], output masks.
+ """
+ x, protos = preds[0], preds[1] # Two outputs: predictions and protos
+
+ # Transpose dim 1: (Batch_size, xywh_conf_cls_nm, Num_anchors) -> (Batch_size, Num_anchors, xywh_conf_cls_nm)
+ x = np.einsum("bcn->bnc", x)
+
+ # Predictions filtering by conf-threshold
+ x = x[np.amax(x[..., 4:-nm], axis=-1) > conf_threshold]
+
+ # Create a new matrix which merge these(box, score, cls, nm) into one
+ # For more details about `numpy.c_()`: https://numpy.org/doc/1.26/reference/generated/numpy.c_.html
+ x = np.c_[x[..., :4], np.amax(x[..., 4:-nm], axis=-1), np.argmax(x[..., 4:-nm], axis=-1), x[..., -nm:]]
+
+ # NMS filtering
+ x = x[cv2.dnn.NMSBoxes(x[:, :4], x[:, 4], conf_threshold, iou_threshold)]
+
+ # Decode and return
+ if len(x) > 0:
+ # Bounding boxes format change: cxcywh -> xyxy
+ x[..., [0, 1]] -= x[..., [2, 3]] / 2
+ x[..., [2, 3]] += x[..., [0, 1]]
+
+ # Rescales bounding boxes from model shape(model_height, model_width) to the shape of original image
+ x[..., :4] -= [pad_w, pad_h, pad_w, pad_h]
+ x[..., :4] /= min(ratio)
+
+ # Bounding boxes boundary clamp
+ x[..., [0, 2]] = x[:, [0, 2]].clip(0, im0.shape[1])
+ x[..., [1, 3]] = x[:, [1, 3]].clip(0, im0.shape[0])
+
+ # Process masks
+ masks = self.process_mask(protos[0], x[:, 6:], x[:, :4], im0.shape)
+
+ # Masks -> Segments(contours)
+ segments = self.masks2segments(masks)
+ return x[..., :6], segments, masks # boxes, segments, masks
+ else:
+ return [], [], []
+
+ @staticmethod
+ def masks2segments(masks):
+ """
+ Takes a list of masks(n,h,w) and returns a list of segments(n,xy), from
+ https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/ops.py.
+
+ Args:
+ masks (numpy.ndarray): the output of the model, which is a tensor of shape (batch_size, 160, 160).
+
+ Returns:
+ segments (List): list of segment masks.
+ """
+ segments = []
+ for x in masks.astype("uint8"):
+ c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[0] # CHAIN_APPROX_SIMPLE
+ if c:
+ c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2)
+ else:
+ c = np.zeros((0, 2)) # no segments found
+ segments.append(c.astype("float32"))
+ return segments
+
+ @staticmethod
+ def crop_mask(masks, boxes):
+ """
+ Takes a mask and a bounding box, and returns a mask that is cropped to the bounding box, from
+ https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/ops.py.
+
+ Args:
+ masks (Numpy.ndarray): [n, h, w] tensor of masks.
+ boxes (Numpy.ndarray): [n, 4] tensor of bbox coordinates in relative point form.
+
+ Returns:
+ (Numpy.ndarray): The masks are being cropped to the bounding box.
+ """
+ n, h, w = masks.shape
+ x1, y1, x2, y2 = np.split(boxes[:, :, None], 4, 1)
+ r = np.arange(w, dtype=x1.dtype)[None, None, :]
+ c = np.arange(h, dtype=x1.dtype)[None, :, None]
+ return masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2))
+
+ def process_mask(self, protos, masks_in, bboxes, im0_shape):
+ """
+ Takes the output of the mask head, and applies the mask to the bounding boxes. This produces masks of higher
+ quality but is slower, from https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/ops.py.
+
+ Args:
+ protos (numpy.ndarray): [mask_dim, mask_h, mask_w].
+ masks_in (numpy.ndarray): [n, mask_dim], n is number of masks after nms.
+ bboxes (numpy.ndarray): bboxes re-scaled to original image shape.
+ im0_shape (tuple): the size of the input image (h,w,c).
+
+ Returns:
+ (numpy.ndarray): The upsampled masks.
+ """
+ c, mh, mw = protos.shape
+ masks = np.matmul(masks_in, protos.reshape((c, -1))).reshape((-1, mh, mw)).transpose(1, 2, 0) # HWN
+ masks = np.ascontiguousarray(masks)
+ masks = self.scale_mask(masks, im0_shape) # re-scale mask from P3 shape to original input image shape
+ masks = np.einsum("HWN -> NHW", masks) # HWN -> NHW
+ masks = self.crop_mask(masks, bboxes)
+ return np.greater(masks, 0.5)
+
+ @staticmethod
+ def scale_mask(masks, im0_shape, ratio_pad=None):
+ """
+ Takes a mask, and resizes it to the original image size, from
+ https://github.com/ultralytics/ultralytics/blob/main/ultralytics/utils/ops.py.
+
+ Args:
+ masks (np.ndarray): resized and padded masks/images, [h, w, num]/[h, w, 3].
+ im0_shape (tuple): the original image shape.
+ ratio_pad (tuple): the ratio of the padding to the original image.
+
+ Returns:
+ masks (np.ndarray): The masks that are being returned.
+ """
+ im1_shape = masks.shape[:2]
+ if ratio_pad is None: # calculate from im0_shape
+ gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new
+ pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding
+ else:
+ pad = ratio_pad[1]
+
+ # Calculate tlbr of mask
+ top, left = int(round(pad[1] - 0.1)), int(round(pad[0] - 0.1)) # y, x
+ bottom, right = int(round(im1_shape[0] - pad[1] + 0.1)), int(round(im1_shape[1] - pad[0] + 0.1))
+ if len(masks.shape) < 2:
+ raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}')
+ masks = masks[top:bottom, left:right]
+ masks = cv2.resize(
+ masks, (im0_shape[1], im0_shape[0]), interpolation=cv2.INTER_LINEAR
+ ) # INTER_CUBIC would be better
+ if len(masks.shape) == 2:
+ masks = masks[:, :, None]
+ return masks
+
+ def draw_and_visualize(self, im, bboxes, segments, vis=False, save=True):
+ """
+ Draw and visualize results.
+
+ Args:
+ im (np.ndarray): original image, shape [h, w, c].
+ bboxes (numpy.ndarray): [n, 4], n is number of bboxes.
+ segments (List): list of segment masks.
+ vis (bool): imshow using OpenCV.
+ save (bool): save image annotated.
+
+ Returns:
+ None
+ """
+ # Draw rectangles and polygons
+ im_canvas = im.copy()
+ for (*box, conf, cls_), segment in zip(bboxes, segments):
+ # draw contour and fill mask
+ cv2.polylines(im, np.int32([segment]), True, (255, 255, 255), 2) # white borderline
+ cv2.fillPoly(im_canvas, np.int32([segment]), self.color_palette(int(cls_), bgr=True))
+
+ # draw bbox rectangle
+ cv2.rectangle(
+ im,
+ (int(box[0]), int(box[1])),
+ (int(box[2]), int(box[3])),
+ self.color_palette(int(cls_), bgr=True),
+ 1,
+ cv2.LINE_AA,
+ )
+ cv2.putText(
+ im,
+ f"{self.classes[cls_]}: {conf:.3f}",
+ (int(box[0]), int(box[1] - 9)),
+ cv2.FONT_HERSHEY_SIMPLEX,
+ 0.7,
+ self.color_palette(int(cls_), bgr=True),
+ 2,
+ cv2.LINE_AA,
+ )
+
+ # Mix image
+ im = cv2.addWeighted(im_canvas, 0.3, im, 0.7, 0)
+
+ # Show image
+ if vis:
+ cv2.imshow("demo", im)
+ cv2.waitKey(0)
+ cv2.destroyAllWindows()
+
+ # Save image
+ if save:
+ cv2.imwrite("demo.jpg", im)
+
+
+if __name__ == "__main__":
+ # Create an argument parser to handle command-line arguments
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--model", type=str, required=True, help="Path to ONNX model")
+ parser.add_argument("--source", type=str, default=str(ASSETS / "bus.jpg"), help="Path to input image")
+ parser.add_argument("--conf", type=float, default=0.25, help="Confidence threshold")
+ parser.add_argument("--iou", type=float, default=0.45, help="NMS IoU threshold")
+ args = parser.parse_args()
+
+ # Build model
+ model = YOLOv8Seg(args.model)
+
+ # Read image by OpenCV
+ img = cv2.imread(args.source)
+
+ # Inference
+ boxes, segments, _ = model(img, conf_threshold=args.conf, iou_threshold=args.iou)
+
+ # Draw bboxes and polygons
+ if len(boxes) > 0:
+ model.draw_and_visualize(img, boxes, segments, vis=False, save=True)
diff --git a/examples/YOLOv8-TFLite-Python/README.md b/examples/YOLOv8-TFLite-Python/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..0156759fdba626189e3c42f3c00556f0f7f1bbe6
--- /dev/null
+++ b/examples/YOLOv8-TFLite-Python/README.md
@@ -0,0 +1,55 @@
+# YOLOv8 - TFLite Runtime
+
+This example shows how to run inference with YOLOv8 TFLite model. It supports FP32, FP16 and INT8 models.
+
+## Installation
+
+### Installing `tflite-runtime`
+
+To load TFLite models, install the `tflite-runtime` package using:
+
+```bash
+pip install tflite-runtime
+```
+
+### Installing `tensorflow-gpu` (For NVIDIA GPU Users)
+
+Leverage GPU acceleration with NVIDIA GPUs by installing `tensorflow-gpu`:
+
+```bash
+pip install tensorflow-gpu
+```
+
+**Note:** Ensure you have compatible GPU drivers installed on your system.
+
+### Installing `tensorflow` (CPU Version)
+
+For CPU usage or non-NVIDIA GPUs, install TensorFlow with:
+
+```bash
+pip install tensorflow
+```
+
+## Usage
+
+Follow these instructions to run YOLOv8 after successful installation.
+
+Convert the YOLOv8 model to TFLite format:
+
+```bash
+yolo export model=yolov8n.pt imgsz=640 format=tflite int8
+```
+
+Locate the TFLite model in `yolov8n_saved_model`. Then, execute the following in your terminal:
+
+```bash
+python main.py --model yolov8n_full_integer_quant.tflite --img image.jpg --conf 0.25 --iou 0.45 --metadata "metadata.yaml"
+```
+
+Replace `best_full_integer_quant.tflite` with the TFLite model path, `image.jpg` with the input image path, `metadata.yaml` with the one generated by `ultralytics` during export, and adjust the confidence (conf) and IoU thresholds (iou) as necessary.
+
+### Output
+
+The output would show the detections along with the class labels and confidences of each detected object.
+
+
diff --git a/examples/YOLOv8-TFLite-Python/main.py b/examples/YOLOv8-TFLite-Python/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..00c403032859477a5a8a49260080214af92d920f
--- /dev/null
+++ b/examples/YOLOv8-TFLite-Python/main.py
@@ -0,0 +1,221 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import argparse
+from typing import Tuple, Union
+
+import cv2
+import numpy as np
+import tensorflow as tf
+import yaml
+
+from ultralytics.utils import ASSETS
+
+try:
+ from tflite_runtime.interpreter import Interpreter
+except ImportError:
+ import tensorflow as tf
+
+ Interpreter = tf.lite.Interpreter
+
+
+class YOLOv8TFLite:
+ """
+ YOLOv8TFLite.
+
+ A class for performing object detection using the YOLOv8 model with TensorFlow Lite.
+
+ Attributes:
+ model (str): Path to the TensorFlow Lite model file.
+ conf (float): Confidence threshold for filtering detections.
+ iou (float): Intersection over Union threshold for non-maximum suppression.
+ metadata (Optional[str]): Path to the metadata file, if any.
+
+ Methods:
+ detect(img_path: str) -> np.ndarray:
+ Performs inference and returns the output image with drawn detections.
+ """
+
+ def __init__(self, model: str, conf: float = 0.25, iou: float = 0.45, metadata: Union[str, None] = None):
+ """
+ Initializes an instance of the YOLOv8TFLite class.
+
+ Args:
+ model (str): Path to the TFLite model.
+ conf (float, optional): Confidence threshold for filtering detections. Defaults to 0.25.
+ iou (float, optional): IoU (Intersection over Union) threshold for non-maximum suppression. Defaults to 0.45.
+ metadata (Union[str, None], optional): Path to the metadata file or None if not used. Defaults to None.
+ """
+ self.conf = conf
+ self.iou = iou
+ if metadata is None:
+ self.classes = {i: i for i in range(1000)}
+ else:
+ with open(metadata) as f:
+ self.classes = yaml.safe_load(f)["names"]
+ np.random.seed(42)
+ self.color_palette = np.random.uniform(128, 255, size=(len(self.classes), 3))
+
+ self.model = Interpreter(model_path=model)
+ self.model.allocate_tensors()
+
+ input_details = self.model.get_input_details()[0]
+
+ self.in_width, self.in_height = input_details["shape"][1:3]
+ self.in_index = input_details["index"]
+ self.in_scale, self.in_zero_point = input_details["quantization"]
+ self.int8 = input_details["dtype"] == np.int8
+
+ output_details = self.model.get_output_details()[0]
+ self.out_index = output_details["index"]
+ self.out_scale, self.out_zero_point = output_details["quantization"]
+
+ def letterbox(self, img: np.ndarray, new_shape: Tuple = (640, 640)) -> Tuple[np.ndarray, Tuple[float, float]]:
+ """Resizes and reshapes images while maintaining aspect ratio by adding padding, suitable for YOLO models."""
+ shape = img.shape[:2] # current shape [height, width]
+
+ # Scale ratio (new / old)
+ r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+
+ # Compute padding
+ new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+ dw, dh = (new_shape[1] - new_unpad[0]) / 2, (new_shape[0] - new_unpad[1]) / 2 # wh padding
+
+ if shape[::-1] != new_unpad: # resize
+ img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
+ top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+ left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+ img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114))
+
+ return img, (top / img.shape[0], left / img.shape[1])
+
+ def draw_detections(self, img: np.ndarray, box: np.ndarray, score: np.float32, class_id: int) -> None:
+ """
+ Draws bounding boxes and labels on the input image based on the detected objects.
+
+ Args:
+ img (np.ndarray): The input image to draw detections on.
+ box (np.ndarray): Detected bounding box in the format [x1, y1, width, height].
+ score (np.float32): Corresponding detection score.
+ class_id (int): Class ID for the detected object.
+
+ Returns:
+ None
+ """
+ x1, y1, w, h = box
+ color = self.color_palette[class_id]
+
+ cv2.rectangle(img, (int(x1), int(y1)), (int(x1 + w), int(y1 + h)), color, 2)
+
+ label = f"{self.classes[class_id]}: {score:.2f}"
+
+ (label_width, label_height), _ = cv2.getTextSize(label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)
+
+ label_x = x1
+ label_y = y1 - 10 if y1 - 10 > label_height else y1 + 10
+
+ cv2.rectangle(
+ img,
+ (int(label_x), int(label_y - label_height)),
+ (int(label_x + label_width), int(label_y + label_height)),
+ color,
+ cv2.FILLED,
+ )
+
+ cv2.putText(img, label, (int(label_x), int(label_y)), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 1, cv2.LINE_AA)
+
+ def preprocess(self, img: np.ndarray) -> Tuple[np.ndarray, Tuple[float, float]]:
+ """
+ Preprocesses the input image before performing inference.
+
+ Args:
+ img (np.ndarray): The input image to be preprocessed.
+
+ Returns:
+ Tuple[np.ndarray, Tuple[float, float]]: A tuple containing:
+ - The preprocessed image (np.ndarray).
+ - A tuple of two float values representing the padding applied (top/bottom, left/right).
+ """
+ img, pad = self.letterbox(img, (self.in_width, self.in_height))
+ img = img[..., ::-1][None] # N,H,W,C for TFLite
+ img = np.ascontiguousarray(img)
+ img = img.astype(np.float32)
+ return img / 255, pad
+
+ def postprocess(self, img: np.ndarray, outputs: np.ndarray, pad: Tuple[float, float]) -> np.ndarray:
+ """
+ Performs post-processing on the model's output to extract bounding boxes, scores, and class IDs.
+
+ Args:
+ img (numpy.ndarray): The input image.
+ outputs (numpy.ndarray): The output of the model.
+ pad (Tuple[float, float]): Padding used by letterbox.
+
+ Returns:
+ numpy.ndarray: The input image with detections drawn on it.
+ """
+ outputs[:, 0] -= pad[1]
+ outputs[:, 1] -= pad[0]
+ outputs[:, :4] *= max(img.shape)
+
+ outputs = outputs.transpose(0, 2, 1)
+ outputs[..., 0] -= outputs[..., 2] / 2
+ outputs[..., 1] -= outputs[..., 3] / 2
+
+ for out in outputs:
+ scores = out[:, 4:].max(-1)
+ keep = scores > self.conf
+ boxes = out[keep, :4]
+ scores = scores[keep]
+ class_ids = out[keep, 4:].argmax(-1)
+
+ indices = cv2.dnn.NMSBoxes(boxes, scores, self.conf, self.iou).flatten()
+
+ [self.draw_detections(img, boxes[i], scores[i], class_ids[i]) for i in indices]
+
+ return img
+
+ def detect(self, img_path: str) -> np.ndarray:
+ """
+ Performs inference using a TFLite model and returns the output image with drawn detections.
+
+ Args:
+ img_path (str): The path to the input image file.
+
+ Returns:
+ np.ndarray: The output image with drawn detections.
+ """
+ img = cv2.imread(img_path)
+ x, pad = self.preprocess(img)
+ if self.int8:
+ x = (x / self.in_scale + self.in_zero_point).astype(np.int8)
+ self.model.set_tensor(self.in_index, x)
+
+ self.model.invoke()
+
+ y = self.model.get_tensor(self.out_index)
+
+ if self.int8:
+ y = (y.astype(np.float32) - self.out_zero_point) * self.out_scale
+
+ return self.postprocess(img, y, pad)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument(
+ "--model",
+ type=str,
+ default="yolov8n_saved_model/yolov8n_full_integer_quant.tflite",
+ help="Path to TFLite model.",
+ )
+ parser.add_argument("--img", type=str, default=str(ASSETS / "bus.jpg"), help="Path to input image")
+ parser.add_argument("--conf", type=float, default=0.25, help="Confidence threshold")
+ parser.add_argument("--iou", type=float, default=0.45, help="NMS IoU threshold")
+ parser.add_argument("--metadata", type=str, default="yolov8n_saved_model/metadata.yaml", help="Metadata yaml")
+ args = parser.parse_args()
+
+ detector = YOLOv8TFLite(args.model, args.conf, args.iou, args.metadata)
+ result = detector.detect(str(ASSETS / "bus.jpg"))
+
+ cv2.imshow("Output", result)
+ cv2.waitKey(0)
diff --git a/examples/heatmaps.ipynb b/examples/heatmaps.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..4f34da35a4c31efa03ecfa593db24a7dae116665
--- /dev/null
+++ b/examples/heatmaps.ipynb
@@ -0,0 +1,186 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PN1cAxdvd61e"
+ },
+ "source": [
+ "![](\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png\")
\n",
+ "\n",
+ " [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)\n",
+ "\n",
+ " ![\"Ultralytics](\"https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml/badge.svg\"){kind=icon}
\n",
+ " ![\"Run](\"https://assets.paperspace.io/img/gradient-badge.svg\"){kind=icon}
\n",
+ " ![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
\n",
+ " ![\"Open](\"https://kaggle.com/static/images/open-in-kaggle.svg\")
\n",
+ " ![\"Discord\"](\"https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue\")
\n",
+ "\n",
+ "Welcome to the Ultralytics YOLO11 🚀 notebook! YOLO11 is the latest version of the YOLO (You Only Look Once) AI models developed by Ultralytics. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLO11 and understand its features and capabilities.\n",
+ "\n",
+ "YOLO11 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n",
+ "\n",
+ "We hope that the resources in this notebook will help you get the most out of YOLO11. Please browse the YOLO11 Heatmap Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!\n",
+ "\n",
+ "![](\"https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png\")
\n",
+ "\n",
+ "![\"CI](\"https://github.com/ultralytics/hub/actions/workflows/ci.yml/badge.svg\"){kind=icon}
\n",
+ " \n",
+ "\n",
+ " \n",
+ " ![\"Ultralytics](\"https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue\")
\n",
+ " ![\"Ultralytics](\"https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue\")
\n",
+ "\n",
+ "Welcome to the [Ultralytics](https://ultralytics.com/) HUB notebook!\n",
+ "\n",
+ "This notebook allows you to train Ultralytics [YOLO](https://github.com/ultralytics/ultralytics) 🚀 models using [HUB](https://hub.ultralytics.com/). Please browse the HUB Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!\n",
+ "\n",
+ "\n",
+ " [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)\n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ "Welcome to the Ultralytics YOLO11 🚀 notebook! YOLO11 is the latest version of the YOLO (You Only Look Once) AI models developed by Ultralytics. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLO11 and understand its features and capabilities.\n",
+ "\n",
+ "YOLO11 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n",
+ "\n",
+ "We hope that the resources in this notebook will help you get the most out of YOLO11. Please browse the YOLO11 Object Counting Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!\n",
+ "\n",
+ "\n",
+ "\n",
+ " [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)\n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ "Welcome to the Ultralytics YOLO11 🚀 notebook! YOLO11 is the latest version of the YOLO (You Only Look Once) AI models developed by Ultralytics. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLO11 and understand its features and capabilities.\n",
+ "\n",
+ "YOLO11 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n",
+ "\n",
+ "We hope that the resources in this notebook will help you get the most out of YOLO11. Please browse the YOLO11 Tracking Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!\n",
+ "\n",
+ "\n",
+ "\n",
+ " [中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [Türkçe](https://docs.ultralytics.com/tr/) | [Tiếng Việt](https://docs.ultralytics.com/vi/) | [العربية](https://docs.ultralytics.com/ar/)\n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ "Welcome to the Ultralytics YOLO11 🚀 notebook! YOLO11 is the latest version of the YOLO (You Only Look Once) AI models developed by Ultralytics. This notebook serves as the starting point for exploring the various resources available to help you get started with YOLO11 and understand its features and capabilities.\n",
+ "\n",
+ "YOLO11 models are fast, accurate, and easy to use, making them ideal for various object detection and image segmentation tasks. They can be trained on large datasets and run on diverse hardware platforms, from CPUs to GPUs.\n",
+ "\n",
+ "We hope that the resources in this notebook will help you get the most out of YOLO11. Please browse the YOLO11 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions!\n",
+ "\n",
+ " \n",
+ " ![\"Ultralytics](\"https://img.youtube.com/vi/ZN3nRZT7b24/maxresdefault.jpg\")
\n",
+ " \n",
+ " \n", + " Watch: How to Train\n", + " Ultralytics\n", + " YOLO11 Model on Custom Dataset using Google Colab Notebook 🚀
\n", + "![](\"https://user-images.githubusercontent.com/26833433/212889447-69e5bdf1-5800-4e29-835e-2ed2336dede2.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0eq1SMWl6Sfn"
+ },
+ "source": [
+ "# 2. Val\n",
+ "Validate a model's accuracy on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset's `val` or `test` splits. The latest YOLO11 [models](https://github.com/ultralytics/ultralytics#models) are downloaded automatically the first time they are used. See [YOLO11 Val Docs](https://docs.ultralytics.com/modes/val/) for more information."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "WQPtK1QYVaD_"
+ },
+ "source": [
+ "# Download COCO val\n",
+ "import torch\n",
+ "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\n",
+ "!unzip -q tmp.zip -d datasets && rm tmp.zip # unzip"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "X58w8JLpMnjH",
+ "outputId": "af2a5deb-029b-466d-96a4-bd3e406987fa",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ }
+ },
+ "source": [
+ "# Validate YOLO11n on COCO8 val\n",
+ "!yolo val model=yolo11n.pt data=coco8.yaml"
+ ],
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Ultralytics 8.3.2 🚀 Python-3.10.12 torch-2.4.1+cu121 CUDA:0 (Tesla T4, 15102MiB)\n",
+ "YOLO11n summary (fused): 238 layers, 2,616,248 parameters, 0 gradients, 6.5 GFLOPs\n",
+ "\n",
+ "Dataset 'coco8.yaml' images not found ⚠️, missing path '/content/datasets/coco8/images/val'\n",
+ "Downloading https://ultralytics.com/assets/coco8.zip to '/content/datasets/coco8.zip'...\n",
+ "100% 433k/433k [00:00<00:00, 15.8MB/s]\n",
+ "Unzipping /content/datasets/coco8.zip to /content/datasets/coco8...: 100% 25/25 [00:00<00:00, 1188.35file/s]\n",
+ "Dataset download success ✅ (1.4s), saved to \u001b[1m/content/datasets\u001b[0m\n",
+ "\n",
+ "Downloading https://ultralytics.com/assets/Arial.ttf to '/root/.config/Ultralytics/Arial.ttf'...\n",
+ "100% 755k/755k [00:00<00:00, 17.7MB/s]\n",
+ "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco8/labels/val... 4 images, 0 backgrounds, 0 corrupt: 100% 4/4 [00:00<00:00, 142.04it/s]\n",
+ "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco8/labels/val.cache\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95): 100% 1/1 [00:04<00:00, 4.75s/it]\n",
+ " all 4 17 0.57 0.85 0.847 0.632\n",
+ " person 3 10 0.557 0.6 0.585 0.272\n",
+ " dog 1 1 0.548 1 0.995 0.697\n",
+ " horse 1 2 0.531 1 0.995 0.674\n",
+ " elephant 1 2 0.371 0.5 0.516 0.256\n",
+ " umbrella 1 1 0.569 1 0.995 0.995\n",
+ " potted plant 1 1 0.847 1 0.995 0.895\n",
+ "Speed: 1.0ms preprocess, 73.8ms inference, 0.0ms loss, 561.4ms postprocess per image\n",
+ "Results saved to \u001b[1mruns/detect/val\u001b[0m\n",
+ "💡 Learn more at https://docs.ultralytics.com/modes/val\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZY2VXXXu74w5"
+ },
+ "source": [
+ "# 3. Train\n",
+ "\n",
+ "![](\"https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png\"/)
![](\"https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-tasks.png\")
\n"
+ ],
+ "metadata": {
+ "id": "Phm9ccmOKye5"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 1. Detection\n",
+ "\n",
+ "YOLO11 _detection_ models have no suffix and are the default YOLO11 models, i.e. `yolo11n.pt` and are pretrained on COCO. See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for full details.\n"
+ ],
+ "metadata": {
+ "id": "yq26lwpYK1lq"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Load YOLO11n, train it on COCO128 for 3 epochs and predict an image with it\n",
+ "from ultralytics import YOLO\n",
+ "\n",
+ "model = YOLO('yolo11n.pt') # load a pretrained YOLO detection model\n",
+ "model.train(data='coco8.yaml', epochs=3) # train the model\n",
+ "model('https://ultralytics.com/images/bus.jpg') # predict on an image"
+ ],
+ "metadata": {
+ "id": "8Go5qqS9LbC5"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 2. Segmentation\n",
+ "\n",
+ "YOLO11 _segmentation_ models use the `-seg` suffix, i.e. `yolo11n-seg.pt` and are pretrained on COCO. See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for full details.\n"
+ ],
+ "metadata": {
+ "id": "7ZW58jUzK66B"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Load YOLO11n-seg, train it on COCO128-seg for 3 epochs and predict an image with it\n",
+ "from ultralytics import YOLO\n",
+ "\n",
+ "model = YOLO('yolo11n-seg.pt') # load a pretrained YOLO segmentation model\n",
+ "model.train(data='coco8-seg.yaml', epochs=3) # train the model\n",
+ "model('https://ultralytics.com/images/bus.jpg') # predict on an image"
+ ],
+ "metadata": {
+ "id": "WFPJIQl_L5HT"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 3. Classification\n",
+ "\n",
+ "YOLO11 _classification_ models use the `-cls` suffix, i.e. `yolo11n-cls.pt` and are pretrained on ImageNet. See [Classification Docs](https://docs.ultralytics.com/tasks/classify/) for full details.\n"
+ ],
+ "metadata": {
+ "id": "ax3p94VNK9zR"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Load YOLO11n-cls, train it on mnist160 for 3 epochs and predict an image with it\n",
+ "from ultralytics import YOLO\n",
+ "\n",
+ "model = YOLO('yolo11n-cls.pt') # load a pretrained YOLO classification model\n",
+ "model.train(data='mnist160', epochs=3) # train the model\n",
+ "model('https://ultralytics.com/images/bus.jpg') # predict on an image"
+ ],
+ "metadata": {
+ "id": "5q9Zu6zlL5rS"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 4. Pose\n",
+ "\n",
+ "YOLO11 _pose_ models use the `-pose` suffix, i.e. `yolo11n-pose.pt` and are pretrained on COCO Keypoints. See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for full details."
+ ],
+ "metadata": {
+ "id": "SpIaFLiO11TG"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Load YOLO11n-pose, train it on COCO8-pose for 3 epochs and predict an image with it\n",
+ "from ultralytics import YOLO\n",
+ "\n",
+ "model = YOLO('yolo11n-pose.pt') # load a pretrained YOLO pose model\n",
+ "model.train(data='coco8-pose.yaml', epochs=3) # train the model\n",
+ "model('https://ultralytics.com/images/bus.jpg') # predict on an image"
+ ],
+ "metadata": {
+ "id": "si4aKFNg19vX"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## 4. Oriented Bounding Boxes (OBB)\n",
+ "\n",
+ "YOLO11 _OBB_ models use the `-obb` suffix, i.e. `yolo11n-obb.pt` and are pretrained on the DOTA dataset. See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for full details."
+ ],
+ "metadata": {
+ "id": "cf5j_T9-B5F0"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Load YOLO11n-obb, train it on DOTA8 for 3 epochs and predict an image with it\n",
+ "from ultralytics import YOLO\n",
+ "\n",
+ "model = YOLO('yolo11n-obb.pt') # load a pretrained YOLO OBB model\n",
+ "model.train(data='dota8.yaml', epochs=3) # train the model\n",
+ "model('https://ultralytics.com/images/boats.jpg') # predict on an image"
+ ],
+ "metadata": {
+ "id": "IJNKClOOB5YS"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IEijrePND_2I"
+ },
+ "source": [
+ "# Appendix\n",
+ "\n",
+ "Additional content below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Pip install from source\n",
+ "!pip install git+https://github.com/ultralytics/ultralytics@main"
+ ],
+ "metadata": {
+ "id": "pIdE6i8C3LYp"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Git clone and run tests on updates branch\n",
+ "!git clone https://github.com/ultralytics/ultralytics -b main\n",
+ "%pip install -qe ultralytics"
+ ],
+ "metadata": {
+ "id": "uRKlwxSJdhd1"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Run tests (Git clone only)\n",
+ "!pytest ultralytics/tests"
+ ],
+ "metadata": {
+ "id": "GtPlh7mcCGZX"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Validate multiple models\n",
+ "for x in 'nsmlx':\n",
+ " !yolo val model=yolo11{x}.pt data=coco.yaml"
+ ],
+ "metadata": {
+ "id": "Wdc6t_bfzDDk"
+ },
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
diff --git a/logs/yolov12l.csv b/logs/yolov12l.csv
new file mode 100644
index 0000000000000000000000000000000000000000..077042f5378a1d96d53e5dec8322cc4b3c15eeb1
--- /dev/null
+++ b/logs/yolov12l.csv
@@ -0,0 +1,601 @@
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diff --git a/logs/yolov12m.csv b/logs/yolov12m.csv
new file mode 100644
index 0000000000000000000000000000000000000000..c2756ad8410fa76014e25db87d4bc9f14afbedd6
--- /dev/null
+++ b/logs/yolov12m.csv
@@ -0,0 +1,601 @@
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diff --git a/logs/yolov12n.csv b/logs/yolov12n.csv
new file mode 100644
index 0000000000000000000000000000000000000000..50d7c2482a073413d28cb0c86bf2d93647619157
--- /dev/null
+++ b/logs/yolov12n.csv
@@ -0,0 +1,601 @@
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diff --git a/logs/yolov12s.csv b/logs/yolov12s.csv
new file mode 100644
index 0000000000000000000000000000000000000000..54520d4095e19325d21f4f39e4d6fa70717bd690
--- /dev/null
+++ b/logs/yolov12s.csv
@@ -0,0 +1,601 @@
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diff --git a/logs/yolov12x.csv b/logs/yolov12x.csv
new file mode 100644
index 0000000000000000000000000000000000000000..ff6a2a1c505ce809601491f6aab43e26cd163190
--- /dev/null
+++ b/logs/yolov12x.csv
@@ -0,0 +1,601 @@
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diff --git a/mkdocs.yml b/mkdocs.yml
new file mode 100644
index 0000000000000000000000000000000000000000..1ac53c83bf82b0beac772ea2a4879f050b0b602f
--- /dev/null
+++ b/mkdocs.yml
@@ -0,0 +1,769 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Configuration file for building the Ultralytics YOLO documentation site using MkDocs.
+# Provides settings to control site metadata, customize the appearance using the
+# Material theme, define the navigation structure, and enable various plugins.
+
+# Site metadata
+site_name: Ultralytics YOLO Docs
+site_description: Explore Ultralytics YOLO, a cutting-edge real-time object detection and image segmentation model for various applications and hardware platforms.
+site_url: https://docs.ultralytics.com
+site_author: Ultralytics
+repo_url: https://github.com/ultralytics/ultralytics
+edit_uri: https://github.com/ultralytics/ultralytics/tree/main/docs/en/
+repo_name: ultralytics/ultralytics
+remote_name: https://github.com/ultralytics/docs
+docs_dir: "docs/en/" # where to find the markdown files
+site_dir: "site/" # where to publish to
+use_directory_urls: true # don't display 'index.html' in slugs
+
+# Theme customization
+theme:
+ name: material
+ language: en
+ custom_dir: docs/overrides/
+ logo: https://raw.githubusercontent.com/ultralytics/assets/main/logo/Ultralytics_Logotype_Reverse.svg
+ favicon: https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/logo/favicon-yolo.png
+ icon:
+ repo: fontawesome/brands/github
+ # font: # disabled for faster page load times
+ # text: Helvetica
+ # code: Roboto Mono
+ palette:
+ - media: "(prefers-color-scheme)"
+ toggle:
+ icon: material/brightness-auto
+ name: Switch to light mode
+ - media: "(prefers-color-scheme: dark)"
+ scheme: slate
+ primary: black
+ accent: indigo
+ toggle:
+ icon: material/brightness-4
+ name: Switch to system preference
+ - media: "(prefers-color-scheme: light)"
+ scheme: default
+ primary: indigo
+ accent: indigo
+ toggle:
+ icon: material/brightness-7
+ name: Switch to dark mode
+ features:
+ - content.action.edit
+ - content.code.annotate
+ - content.code.copy
+ - content.tooltips
+ - search.highlight
+ - search.share
+ - search.suggest
+ - toc.follow
+ - navigation.top
+ - navigation.tabs
+ - navigation.tabs.sticky
+ - navigation.prune
+ - navigation.footer
+ - navigation.tracking
+ - navigation.instant
+ - navigation.instant.progress
+ - navigation.indexes
+ - navigation.sections # navigation.expand or navigation.sections
+ - content.tabs.link # all code tabs change simultaneously
+
+# Customization
+copyright: © 2025 Ultralytics Inc. All rights reserved.
+extra: # version:
+ homepage: https://www.ultralytics.com/
+ # provider: mike # version drop-down menu
+ robots: robots.txt
+ analytics:
+ provider: google
+ property: G-2M5EHKC0BH
+ social:
+ - icon: fontawesome/brands/github
+ link: https://github.com/ultralytics
+ - icon: fontawesome/brands/linkedin
+ link: https://www.linkedin.com/company/ultralytics/
+ - icon: fontawesome/brands/x-twitter
+ link: https://twitter.com/ultralytics
+ - icon: fontawesome/brands/youtube
+ link: https://youtube.com/ultralytics?sub_confirmation=1
+ - icon: fontawesome/brands/docker
+ link: https://hub.docker.com/r/ultralytics/ultralytics/
+ - icon: fontawesome/brands/python
+ link: https://pypi.org/project/ultralytics/
+ - icon: fontawesome/brands/discord
+ link: https://discord.com/invite/ultralytics
+ - icon: fontawesome/brands/reddit
+ link: https://reddit.com/r/ultralytics
+
+extra_css:
+ - stylesheets/style.css
+
+extra_javascript:
+ - javascript/extra.js
+ - javascript/giscus.js
+
+markdown_extensions:
+ - admonition
+ - md_in_html
+ - tables
+ - attr_list
+ - def_list
+ - pymdownx.critic
+ - pymdownx.caret
+ - pymdownx.keys
+ - pymdownx.mark
+ - pymdownx.tilde
+ - pymdownx.details
+ - pymdownx.superfences
+ - pymdownx.inlinehilite
+ - pymdownx.highlight:
+ anchor_linenums: true
+ - pymdownx.snippets:
+ base_path: ./
+ - pymdownx.emoji:
+ emoji_index: !!python/name:material.extensions.emoji.twemoji
+ emoji_generator: !!python/name:material.extensions.emoji.to_svg
+ - pymdownx.tabbed:
+ alternate_style: true
+
+# Validation settings https://www.mkdocs.org/user-guide/configuration/#validation
+validation:
+ nav:
+ omitted_files: info
+ not_found: warn
+ absolute_links: info
+ links:
+ absolute_links: relative_to_docs
+ anchors: warn
+ unrecognized_links: warn
+
+# Primary navigation ---------------------------------------------------------------------------------------------------
+nav:
+ - Home:
+ - Home: index.md
+ - Quickstart: quickstart.md
+ - Modes:
+ - modes/index.md
+ - Train: modes/train.md
+ - Val: modes/val.md
+ - Predict: modes/predict.md
+ - Export: modes/export.md
+ - Track: modes/track.md
+ - Benchmark: modes/benchmark.md
+ - Tasks:
+ - tasks/index.md
+ - Detect: tasks/detect.md
+ - Segment: tasks/segment.md
+ - Classify: tasks/classify.md
+ - Pose: tasks/pose.md
+ - OBB: tasks/obb.md
+ - Models:
+ - models/index.md
+ - Datasets:
+ - datasets/index.md
+ - Solutions:
+ - solutions/index.md
+ - Guides:
+ - guides/index.md
+ - YOLO11 🚀 NEW: models/yolo11.md # for promotion of new pages
+ - Languages:
+ - 🇬🇧 English: https://ultralytics.com/docs/
+ - 🇨🇳 简体中文: https://docs.ultralytics.com/zh/
+ - 🇰🇷 한국어: https://docs.ultralytics.com/ko/
+ - 🇯🇵 日本語: https://docs.ultralytics.com/ja/
+ - 🇷🇺 Русский: https://docs.ultralytics.com/ru/
+ - 🇩🇪 Deutsch: https://docs.ultralytics.com/de/
+ - 🇫🇷 Français: https://docs.ultralytics.com/fr/
+ - 🇪🇸 Español: https://docs.ultralytics.com/es/
+ - 🇵🇹 Português: https://docs.ultralytics.com/pt/
+ - 🇮🇹 Italiano: https://docs.ultralytics.com/it/
+ - 🇹🇷 Türkçe: https://docs.ultralytics.com/tr/
+ - 🇻🇳 Tiếng Việt: https://docs.ultralytics.com/vi/
+ - 🇸🇦 العربية: https://docs.ultralytics.com/ar/
+ - Quickstart:
+ - quickstart.md
+ - Usage:
+ - CLI: usage/cli.md
+ - Python: usage/python.md
+ - Callbacks: usage/callbacks.md
+ - Configuration: usage/cfg.md
+ - Simple Utilities: usage/simple-utilities.md
+ - Advanced Customization: usage/engine.md
+ - Modes:
+ - modes/index.md
+ - Train: modes/train.md
+ - Val: modes/val.md
+ - Predict: modes/predict.md
+ - Export: modes/export.md
+ - Track: modes/track.md
+ - Benchmark: modes/benchmark.md
+ - Tasks:
+ - tasks/index.md
+ - Detect: tasks/detect.md
+ - Segment: tasks/segment.md
+ - Classify: tasks/classify.md
+ - Pose: tasks/pose.md
+ - OBB: tasks/obb.md
+ - Models:
+ - models/index.md
+ - Datasets:
+ - datasets/index.md
+ - Solutions:
+ - solutions/index.md
+ - Guides:
+ - guides/index.md
+ - Modes:
+ - modes/index.md
+ - Train: modes/train.md
+ - Val: modes/val.md
+ - Predict: modes/predict.md
+ - Export: modes/export.md
+ - Track: modes/track.md
+ - Benchmark: modes/benchmark.md
+ - Tasks:
+ - tasks/index.md
+ - Detect: tasks/detect.md
+ - Segment: tasks/segment.md
+ - Classify: tasks/classify.md
+ - Pose: tasks/pose.md
+ - OBB: tasks/obb.md
+ - Tasks:
+ - tasks/index.md
+ - Detect: tasks/detect.md
+ - Segment: tasks/segment.md
+ - Classify: tasks/classify.md
+ - Pose: tasks/pose.md
+ - OBB: tasks/obb.md
+ - Modes:
+ - modes/index.md
+ - Train: modes/train.md
+ - Val: modes/val.md
+ - Predict: modes/predict.md
+ - Export: modes/export.md
+ - Track: modes/track.md
+ - Benchmark: modes/benchmark.md
+ - Models:
+ - models/index.md
+ - YOLOv3: models/yolov3.md
+ - YOLOv4: models/yolov4.md
+ - YOLOv5: models/yolov5.md
+ - YOLOv6: models/yolov6.md
+ - YOLOv7: models/yolov7.md
+ - YOLOv8: models/yolov8.md
+ - YOLOv9: models/yolov9.md
+ - YOLOv10: models/yolov10.md
+ - YOLO11 🚀 NEW: models/yolo11.md
+ - SAM (Segment Anything Model): models/sam.md
+ - SAM 2 (Segment Anything Model 2): models/sam-2.md
+ - MobileSAM (Mobile Segment Anything Model): models/mobile-sam.md
+ - FastSAM (Fast Segment Anything Model): models/fast-sam.md
+ - YOLO-NAS (Neural Architecture Search): models/yolo-nas.md
+ - RT-DETR (Realtime Detection Transformer): models/rtdetr.md
+ - YOLO-World (Real-Time Open-Vocabulary Object Detection): models/yolo-world.md
+ - Datasets:
+ - datasets/index.md
+ - Detection:
+ - datasets/detect/index.md
+ - Argoverse: datasets/detect/argoverse.md
+ - COCO: datasets/detect/coco.md
+ - LVIS: datasets/detect/lvis.md
+ - COCO8: datasets/detect/coco8.md
+ - GlobalWheat2020: datasets/detect/globalwheat2020.md
+ - Objects365: datasets/detect/objects365.md
+ - OpenImagesV7: datasets/detect/open-images-v7.md
+ - SKU-110K: datasets/detect/sku-110k.md
+ - VisDrone: datasets/detect/visdrone.md
+ - VOC: datasets/detect/voc.md
+ - xView: datasets/detect/xview.md
+ - RF100: datasets/detect/roboflow-100.md
+ - Brain-tumor: datasets/detect/brain-tumor.md
+ - African-wildlife: datasets/detect/african-wildlife.md
+ - Signature: datasets/detect/signature.md
+ - Medical-pills: datasets/detect/medical-pills.md
+ - Segmentation:
+ - datasets/segment/index.md
+ - COCO: datasets/segment/coco.md
+ - COCO8-seg: datasets/segment/coco8-seg.md
+ - Crack-seg: datasets/segment/crack-seg.md
+ - Carparts-seg: datasets/segment/carparts-seg.md
+ - Package-seg: datasets/segment/package-seg.md
+ - Pose:
+ - datasets/pose/index.md
+ - COCO: datasets/pose/coco.md
+ - COCO8-pose: datasets/pose/coco8-pose.md
+ - Tiger-pose: datasets/pose/tiger-pose.md
+ - Hand-keypoints: datasets/pose/hand-keypoints.md
+ - Dog-pose: datasets/pose/dog-pose.md
+ - Classification:
+ - datasets/classify/index.md
+ - Caltech 101: datasets/classify/caltech101.md
+ - Caltech 256: datasets/classify/caltech256.md
+ - CIFAR-10: datasets/classify/cifar10.md
+ - CIFAR-100: datasets/classify/cifar100.md
+ - Fashion-MNIST: datasets/classify/fashion-mnist.md
+ - ImageNet: datasets/classify/imagenet.md
+ - ImageNet-10: datasets/classify/imagenet10.md
+ - Imagenette: datasets/classify/imagenette.md
+ - Imagewoof: datasets/classify/imagewoof.md
+ - MNIST: datasets/classify/mnist.md
+ - Oriented Bounding Boxes (OBB):
+ - datasets/obb/index.md
+ - DOTAv2: datasets/obb/dota-v2.md
+ - DOTA8: datasets/obb/dota8.md
+ - Multi-Object Tracking:
+ - datasets/track/index.md
+ - Solutions 🚀 NEW:
+ - solutions/index.md
+ - Object Counting: guides/object-counting.md
+ - Object Cropping: guides/object-cropping.md
+ - Object Blurring: guides/object-blurring.md
+ - Workouts Monitoring: guides/workouts-monitoring.md
+ - Objects Counting in Regions: guides/region-counting.md
+ - Security Alarm System: guides/security-alarm-system.md
+ - Heatmaps: guides/heatmaps.md
+ - Instance Segmentation with Object Tracking: guides/instance-segmentation-and-tracking.md
+ - VisionEye Mapping: guides/vision-eye.md
+ - Speed Estimation: guides/speed-estimation.md
+ - Distance Calculation: guides/distance-calculation.md
+ - Queue Management: guides/queue-management.md
+ - Parking Management: guides/parking-management.md
+ - Analytics: guides/analytics.md
+ - Live Inference: guides/streamlit-live-inference.md
+ - Track Objects in Zone 🚀 NEW: guides/trackzone.md
+ - Guides:
+ - guides/index.md
+ - YOLO Common Issues: guides/yolo-common-issues.md
+ - YOLO Performance Metrics: guides/yolo-performance-metrics.md
+ - YOLO Thread-Safe Inference: guides/yolo-thread-safe-inference.md
+ - Model Deployment Options: guides/model-deployment-options.md
+ - K-Fold Cross Validation: guides/kfold-cross-validation.md
+ - Hyperparameter Tuning: guides/hyperparameter-tuning.md
+ - SAHI Tiled Inference: guides/sahi-tiled-inference.md
+ - AzureML Quickstart: guides/azureml-quickstart.md
+ - Conda Quickstart: guides/conda-quickstart.md
+ - Docker Quickstart: guides/docker-quickstart.md
+ - Raspberry Pi: guides/raspberry-pi.md
+ - NVIDIA Jetson: guides/nvidia-jetson.md
+ - DeepStream on NVIDIA Jetson: guides/deepstream-nvidia-jetson.md
+ - Triton Inference Server: guides/triton-inference-server.md
+ - Isolating Segmentation Objects: guides/isolating-segmentation-objects.md
+ - Edge TPU on Raspberry Pi: guides/coral-edge-tpu-on-raspberry-pi.md
+ - Viewing Inference Images in a Terminal: guides/view-results-in-terminal.md
+ - OpenVINO Latency vs Throughput modes: guides/optimizing-openvino-latency-vs-throughput-modes.md
+ - ROS Quickstart: guides/ros-quickstart.md
+ - Steps of a Computer Vision Project: guides/steps-of-a-cv-project.md
+ - Defining A Computer Vision Project's Goals: guides/defining-project-goals.md
+ - Data Collection and Annotation: guides/data-collection-and-annotation.md
+ - Preprocessing Annotated Data: guides/preprocessing_annotated_data.md
+ - Tips for Model Training: guides/model-training-tips.md
+ - Insights on Model Evaluation and Fine-Tuning: guides/model-evaluation-insights.md
+ - A Guide on Model Testing: guides/model-testing.md
+ - Best Practices for Model Deployment: guides/model-deployment-practices.md
+ - Maintaining Your Computer Vision Model: guides/model-monitoring-and-maintenance.md
+ - Explorer:
+ - datasets/explorer/index.md
+ - Explorer API: datasets/explorer/api.md
+ - Explorer Dashboard Demo: datasets/explorer/dashboard.md
+ - VOC Exploration Example: datasets/explorer/explorer.md
+ - YOLOv5:
+ - yolov5/index.md
+ - Quickstart: yolov5/quickstart_tutorial.md
+ - Environments:
+ - Amazon Web Services (AWS): yolov5/environments/aws_quickstart_tutorial.md
+ - Google Cloud (GCP): yolov5/environments/google_cloud_quickstart_tutorial.md
+ - AzureML: yolov5/environments/azureml_quickstart_tutorial.md
+ - Docker Image: yolov5/environments/docker_image_quickstart_tutorial.md
+ - Tutorials:
+ - Train Custom Data: yolov5/tutorials/train_custom_data.md
+ - Tips for Best Training Results: yolov5/tutorials/tips_for_best_training_results.md
+ - Multi-GPU Training: yolov5/tutorials/multi_gpu_training.md
+ - PyTorch Hub: yolov5/tutorials/pytorch_hub_model_loading.md
+ - TFLite, ONNX, CoreML, TensorRT Export: yolov5/tutorials/model_export.md
+ - Test-Time Augmentation (TTA): yolov5/tutorials/test_time_augmentation.md
+ - Model Ensembling: yolov5/tutorials/model_ensembling.md
+ - Pruning/Sparsity Tutorial: yolov5/tutorials/model_pruning_and_sparsity.md
+ - Hyperparameter evolution: yolov5/tutorials/hyperparameter_evolution.md
+ - Transfer learning with frozen layers: yolov5/tutorials/transfer_learning_with_frozen_layers.md
+ - Architecture Summary: yolov5/tutorials/architecture_description.md
+ - Roboflow Datasets: yolov5/tutorials/roboflow_datasets_integration.md
+ - Neural Magic's DeepSparse: yolov5/tutorials/neural_magic_pruning_quantization.md
+ - Comet Logging: yolov5/tutorials/comet_logging_integration.md
+ - Clearml Logging: yolov5/tutorials/clearml_logging_integration.md
+ - Integrations:
+ - integrations/index.md
+ - Amazon SageMaker: integrations/amazon-sagemaker.md
+ - ClearML: integrations/clearml.md
+ - Comet ML: integrations/comet.md
+ - CoreML: integrations/coreml.md
+ - DVC: integrations/dvc.md
+ - Google Colab: integrations/google-colab.md
+ - Gradio: integrations/gradio.md
+ - IBM Watsonx: integrations/ibm-watsonx.md
+ - JupyterLab: integrations/jupyterlab.md
+ - Kaggle: integrations/kaggle.md
+ - MLflow: integrations/mlflow.md
+ - Neural Magic: integrations/neural-magic.md
+ - ONNX: integrations/onnx.md
+ - OpenVINO: integrations/openvino.md
+ - PaddlePaddle: integrations/paddlepaddle.md
+ - MNN: integrations/mnn.md
+ - NCNN: integrations/ncnn.md
+ - Paperspace Gradient: integrations/paperspace.md
+ - Ray Tune: integrations/ray-tune.md
+ - Roboflow: integrations/roboflow.md
+ - TF GraphDef: integrations/tf-graphdef.md
+ - TF SavedModel: integrations/tf-savedmodel.md
+ - TF.js: integrations/tfjs.md
+ - TFLite: integrations/tflite.md
+ - TFLite Edge TPU: integrations/edge-tpu.md
+ - TensorBoard: integrations/tensorboard.md
+ - TensorRT: integrations/tensorrt.md
+ - TorchScript: integrations/torchscript.md
+ - VS Code: integrations/vscode.md
+ - Weights & Biases: integrations/weights-biases.md
+ - Albumentations: integrations/albumentations.md
+ - SONY IMX500: integrations/sony-imx500.md
+ - HUB:
+ - hub/index.md
+ - Web:
+ - hub/index.md
+ - Quickstart: hub/quickstart.md
+ - Datasets: hub/datasets.md
+ - Projects: hub/projects.md
+ - Models: hub/models.md
+ - Pro: hub/pro.md
+ - Cloud Training: hub/cloud-training.md
+ - Inference API: hub/inference-api.md
+ - Teams: hub/teams.md
+ - Integrations: hub/integrations.md
+ - App:
+ - hub/app/index.md
+ - iOS: hub/app/ios.md
+ - Android: hub/app/android.md
+ - Python SDK:
+ - hub/sdk/index.md
+ - Quickstart: hub/sdk/quickstart.md
+ - Model: hub/sdk/model.md
+ - Dataset: hub/sdk/dataset.md
+ - Project: hub/sdk/project.md
+ - Reference:
+ - base:
+ - api_client: hub/sdk/reference/base/api_client.md
+ - auth: hub/sdk/reference/base/auth.md
+ - crud_client: hub/sdk/reference/base/crud_client.md
+ - paginated_list: hub/sdk/reference/base/paginated_list.md
+ - server_clients: hub/sdk/reference/base/server_clients.md
+ - helpers:
+ - error_handler: hub/sdk/reference/helpers/error_handler.md
+ - exceptions: hub/sdk/reference/helpers/exceptions.md
+ - logger: hub/sdk/reference/helpers/logger.md
+ - utils: hub/sdk/reference/helpers/utils.md
+ - hub_client: hub/sdk/reference/hub_client.md
+ - modules:
+ - datasets: hub/sdk/reference/modules/datasets.md
+ - models: hub/sdk/reference/modules/models.md
+ - projects: hub/sdk/reference/modules/projects.md
+ - teams: hub/sdk/reference/modules/teams.md
+ - users: hub/sdk/reference/modules/users.md
+ - REST API:
+ - hub/api/index.md
+
+ - Reference:
+ - cfg:
+ - __init__: reference/cfg/__init__.md
+ - data:
+ - annotator: reference/data/annotator.md
+ - augment: reference/data/augment.md
+ - base: reference/data/base.md
+ - build: reference/data/build.md
+ - converter: reference/data/converter.md
+ - dataset: reference/data/dataset.md
+ - loaders: reference/data/loaders.md
+ - split_dota: reference/data/split_dota.md
+ - utils: reference/data/utils.md
+ - engine:
+ - exporter: reference/engine/exporter.md
+ - model: reference/engine/model.md
+ - predictor: reference/engine/predictor.md
+ - results: reference/engine/results.md
+ - trainer: reference/engine/trainer.md
+ - tuner: reference/engine/tuner.md
+ - validator: reference/engine/validator.md
+ - hub:
+ - __init__: reference/hub/__init__.md
+ - auth: reference/hub/auth.md
+ - google:
+ - __init__: reference/hub/google/__init__.md
+ - session: reference/hub/session.md
+ - utils: reference/hub/utils.md
+ - models:
+ - fastsam:
+ - model: reference/models/fastsam/model.md
+ - predict: reference/models/fastsam/predict.md
+ - utils: reference/models/fastsam/utils.md
+ - val: reference/models/fastsam/val.md
+ - nas:
+ - model: reference/models/nas/model.md
+ - predict: reference/models/nas/predict.md
+ - val: reference/models/nas/val.md
+ - rtdetr:
+ - model: reference/models/rtdetr/model.md
+ - predict: reference/models/rtdetr/predict.md
+ - train: reference/models/rtdetr/train.md
+ - val: reference/models/rtdetr/val.md
+ - sam:
+ - amg: reference/models/sam/amg.md
+ - build: reference/models/sam/build.md
+ - model: reference/models/sam/model.md
+ - modules:
+ - blocks: reference/models/sam/modules/blocks.md
+ - decoders: reference/models/sam/modules/decoders.md
+ - encoders: reference/models/sam/modules/encoders.md
+ - memory_attention: reference/models/sam/modules/memory_attention.md
+ - sam: reference/models/sam/modules/sam.md
+ - tiny_encoder: reference/models/sam/modules/tiny_encoder.md
+ - transformer: reference/models/sam/modules/transformer.md
+ - utils: reference/models/sam/modules/utils.md
+ - predict: reference/models/sam/predict.md
+ - utils:
+ - loss: reference/models/utils/loss.md
+ - ops: reference/models/utils/ops.md
+ - yolo:
+ - classify:
+ - predict: reference/models/yolo/classify/predict.md
+ - train: reference/models/yolo/classify/train.md
+ - val: reference/models/yolo/classify/val.md
+ - detect:
+ - predict: reference/models/yolo/detect/predict.md
+ - train: reference/models/yolo/detect/train.md
+ - val: reference/models/yolo/detect/val.md
+ - model: reference/models/yolo/model.md
+ - obb:
+ - predict: reference/models/yolo/obb/predict.md
+ - train: reference/models/yolo/obb/train.md
+ - val: reference/models/yolo/obb/val.md
+ - pose:
+ - predict: reference/models/yolo/pose/predict.md
+ - train: reference/models/yolo/pose/train.md
+ - val: reference/models/yolo/pose/val.md
+ - segment:
+ - predict: reference/models/yolo/segment/predict.md
+ - train: reference/models/yolo/segment/train.md
+ - val: reference/models/yolo/segment/val.md
+ - world:
+ - train: reference/models/yolo/world/train.md
+ - train_world: reference/models/yolo/world/train_world.md
+ - nn:
+ - autobackend: reference/nn/autobackend.md
+ - modules:
+ - activation: reference/nn/modules/activation.md
+ - block: reference/nn/modules/block.md
+ - conv: reference/nn/modules/conv.md
+ - head: reference/nn/modules/head.md
+ - transformer: reference/nn/modules/transformer.md
+ - utils: reference/nn/modules/utils.md
+ - tasks: reference/nn/tasks.md
+ - solutions:
+ - ai_gym: reference/solutions/ai_gym.md
+ - analytics: reference/solutions/analytics.md
+ - distance_calculation: reference/solutions/distance_calculation.md
+ - heatmap: reference/solutions/heatmap.md
+ - object_counter: reference/solutions/object_counter.md
+ - parking_management: reference/solutions/parking_management.md
+ - queue_management: reference/solutions/queue_management.md
+ - region_counter: reference/solutions/region_counter.md
+ - security_alarm: reference/solutions/security_alarm.md
+ - solutions: reference/solutions/solutions.md
+ - speed_estimation: reference/solutions/speed_estimation.md
+ - streamlit_inference: reference/solutions/streamlit_inference.md
+ - trackzone: reference/solutions/trackzone.md
+ - trackers:
+ - basetrack: reference/trackers/basetrack.md
+ - bot_sort: reference/trackers/bot_sort.md
+ - byte_tracker: reference/trackers/byte_tracker.md
+ - track: reference/trackers/track.md
+ - utils:
+ - gmc: reference/trackers/utils/gmc.md
+ - kalman_filter: reference/trackers/utils/kalman_filter.md
+ - matching: reference/trackers/utils/matching.md
+ - utils:
+ - __init__: reference/utils/__init__.md
+ - autobatch: reference/utils/autobatch.md
+ - benchmarks: reference/utils/benchmarks.md
+ - callbacks:
+ - base: reference/utils/callbacks/base.md
+ - clearml: reference/utils/callbacks/clearml.md
+ - comet: reference/utils/callbacks/comet.md
+ - dvc: reference/utils/callbacks/dvc.md
+ - hub: reference/utils/callbacks/hub.md
+ - mlflow: reference/utils/callbacks/mlflow.md
+ - neptune: reference/utils/callbacks/neptune.md
+ - raytune: reference/utils/callbacks/raytune.md
+ - tensorboard: reference/utils/callbacks/tensorboard.md
+ - wb: reference/utils/callbacks/wb.md
+ - checks: reference/utils/checks.md
+ - dist: reference/utils/dist.md
+ - downloads: reference/utils/downloads.md
+ - errors: reference/utils/errors.md
+ - files: reference/utils/files.md
+ - instance: reference/utils/instance.md
+ - loss: reference/utils/loss.md
+ - metrics: reference/utils/metrics.md
+ - ops: reference/utils/ops.md
+ - patches: reference/utils/patches.md
+ - plotting: reference/utils/plotting.md
+ - tal: reference/utils/tal.md
+ - torch_utils: reference/utils/torch_utils.md
+ - triton: reference/utils/triton.md
+ - tuner: reference/utils/tuner.md
+
+ - Help:
+ - Help: help/index.md
+ - Frequently Asked Questions (FAQ): help/FAQ.md
+ - Contributing Guide: help/contributing.md
+ - Continuous Integration (CI) Guide: help/CI.md
+ - Contributor License Agreement (CLA): help/CLA.md
+ - Minimum Reproducible Example (MRE) Guide: help/minimum-reproducible-example.md
+ - Code of Conduct: help/code-of-conduct.md
+ - Environmental, Health and Safety (EHS) Policy: help/environmental-health-safety.md
+ - Security Policy: help/security.md
+ - Privacy Policy: help/privacy.md
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+ tutorials/model-ensembling.md: yolov5/tutorials/model_ensembling.md
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+ tutorials/train-custom-datasets.md: yolov5/tutorials/train_custom_data.md
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+ tutorials/transfer-learning-froze-layers.md: yolov5/tutorials/transfer_learning_with_frozen_layers.md
+ tutorials/weights-and-biasis-logging.md: yolov5/tutorials/comet_logging_integration.md
+ yolov5/pytorch_hub.md: yolov5/tutorials/pytorch_hub_model_loading.md
+ yolov5/hyp_evolution.md: yolov5/tutorials/hyperparameter_evolution.md
+ yolov5/pruning_sparsity.md: yolov5/tutorials/model_pruning_and_sparsity.md
+ yolov5/roboflow.md: yolov5/tutorials/roboflow_datasets_integration.md
+ yolov5/comet.md: yolov5/tutorials/comet_logging_integration.md
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+ yolov5/neural_magic.md: yolov5/tutorials/neural_magic_pruning_quantization.md
+ yolov5/train_custom_data.md: yolov5/tutorials/train_custom_data.md
+ yolov5/architecture.md: yolov5/tutorials/architecture_description.md
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+ yolov5/tutorials/yolov5_test_time_augmentation_tutorial.md: yolov5/tutorials/test_time_augmentation.md
+ yolov5/environments/yolov5_amazon_web_services_quickstart_tutorial.md: yolov5/environments/aws_quickstart_tutorial.md
+ yolov5/environments/yolov5_google_cloud_platform_quickstart_tutorial.md: yolov5/environments/google_cloud_quickstart_tutorial.md
+ yolov5/environments/yolov5_docker_image_quickstart_tutorial.md: yolov5/environments/docker_image_quickstart_tutorial.md
+ reference/data/explorer/explorer.md: datasets/explorer/index.md
+ reference/data/explorer/gui/dash.md: datasets/explorer/index.md
+ reference/data/explorer/utils.md: datasets/explorer/index.md
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..705a2eb80c361b722f277bec6fe37cf01d262584
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,186 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Overview:
+# This pyproject.toml file manages the build, packaging, and distribution of the Ultralytics library.
+# It defines essential project metadata, dependencies, and settings used to develop and deploy the library.
+
+# Key Sections:
+# - [build-system]: Specifies the build requirements and backend (e.g., setuptools, wheel).
+# - [project]: Includes details like name, version, description, authors, dependencies and more.
+# - [project.optional-dependencies]: Provides additional, optional packages for extended features.
+# - [tool.*]: Configures settings for various tools (pytest, yapf, etc.) used in the project.
+
+# Installation:
+# The Ultralytics library can be installed using the command: 'pip install ultralytics'
+# For development purposes, you can install the package in editable mode with: 'pip install -e .'
+# This approach allows for real-time code modifications without the need for re-installation.
+
+# Documentation:
+# For comprehensive documentation and usage instructions, visit: https://docs.ultralytics.com
+
+[build-system]
+requires = ["setuptools>=70.0.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+# Project settings -----------------------------------------------------------------------------------------------------
+[project]
+name = "ultralytics"
+dynamic = ["version"]
+description = "Ultralytics YOLO 🚀 for SOTA object detection, multi-object tracking, instance segmentation, pose estimation and image classification."
+readme = "README.md"
+requires-python = ">=3.8"
+license = { "text" = "AGPL-3.0" }
+keywords = ["machine-learning", "deep-learning", "computer-vision", "ML", "DL", "AI", "YOLO", "YOLOv3", "YOLOv5", "YOLOv8", "YOLOv9", "YOLOv10", "YOLO11", "HUB", "Ultralytics"]
+authors = [
+ { name = "Glenn Jocher", email = "glenn.jocher@ultralytics.com" },
+ { name = "Jing Qiu", email = "jing.qiu@ultralytics.com" },
+]
+maintainers = [
+ { name = "Ultralytics", email = "hello@ultralytics.com" },
+]
+classifiers = [
+ "Development Status :: 4 - Beta",
+ "Intended Audience :: Developers",
+ "Intended Audience :: Education",
+ "Intended Audience :: Science/Research",
+ "License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)",
+ "Programming Language :: Python :: 3",
+ "Programming Language :: Python :: 3.8",
+ "Programming Language :: Python :: 3.9",
+ "Programming Language :: Python :: 3.10",
+ "Programming Language :: Python :: 3.11",
+ "Programming Language :: Python :: 3.12",
+ "Topic :: Software Development",
+ "Topic :: Scientific/Engineering",
+ "Topic :: Scientific/Engineering :: Artificial Intelligence",
+ "Topic :: Scientific/Engineering :: Image Recognition",
+ "Operating System :: POSIX :: Linux",
+ "Operating System :: MacOS",
+ "Operating System :: Microsoft :: Windows",
+]
+
+# Required dependencies ------------------------------------------------------------------------------------------------
+dependencies = [
+ "numpy>=1.23.0",
+ "numpy<2.0.0; sys_platform == 'darwin'", # macOS OpenVINO errors https://github.com/ultralytics/ultralytics/pull/17221
+ "matplotlib>=3.3.0",
+ "opencv-python>=4.6.0",
+ "pillow>=7.1.2",
+ "pyyaml>=5.3.1",
+ "requests>=2.23.0",
+ "scipy>=1.4.1",
+ "torch>=1.8.0",
+ "torch>=1.8.0,!=2.4.0; sys_platform == 'win32'", # Windows CPU errors w/ 2.4.0 https://github.com/ultralytics/ultralytics/issues/15049
+ "torchvision>=0.9.0",
+ "tqdm>=4.64.0", # progress bars
+ "psutil", # system utilization
+ "py-cpuinfo", # display CPU info
+ "pandas>=1.1.4",
+ "seaborn>=0.11.0", # plotting
+ "ultralytics-thop>=2.0.0", # FLOPs computation https://github.com/ultralytics/thop
+]
+
+# Optional dependencies ------------------------------------------------------------------------------------------------
+[project.optional-dependencies]
+dev = [
+ "ipython",
+ "pytest",
+ "pytest-cov",
+ "coverage[toml]",
+ "mkdocs>=1.6.0",
+ "mkdocs-material>=9.5.9",
+ "mkdocstrings[python]",
+ "mkdocs-redirects", # 301 redirects
+ "mkdocs-ultralytics-plugin>=0.1.8", # for meta descriptions and images, dates and authors
+ "mkdocs-macros-plugin>=1.0.5" # duplicating content (i.e. export tables) in multiple places
+]
+export = [
+ "onnx>=1.12.0", # ONNX export
+ "coremltools>=7.0; platform_system != 'Windows' and python_version <= '3.11'", # CoreML supported on macOS and Linux
+ "scikit-learn>=1.3.2; platform_system != 'Windows' and python_version <= '3.11'", # CoreML k-means quantization
+ "openvino>=2024.0.0", # OpenVINO export
+ "tensorflow>=2.0.0", # TF bug https://github.com/ultralytics/ultralytics/issues/5161
+ "tensorflowjs>=3.9.0", # TF.js export, automatically installs tensorflow
+ "tensorstore>=0.1.63; platform_machine == 'aarch64' and python_version >= '3.9'", # for TF Raspberry Pi exports
+ "keras", # not installed automatically by tensorflow>=2.16
+ "flatbuffers>=23.5.26,<100; platform_machine == 'aarch64'", # update old 'flatbuffers' included inside tensorflow package
+ "numpy==1.23.5; platform_machine == 'aarch64'", # fix error: `np.bool` was a deprecated alias for the builtin `bool` when using TensorRT models on NVIDIA Jetson
+ "h5py!=3.11.0; platform_machine == 'aarch64'", # fix h5py build issues due to missing aarch64 wheels in 3.11 release
+]
+solutions = [
+ "shapely>=2.0.0", # shapely for point and polygon data matching
+ "streamlit", # for live inference on web browser i.e `yolo streamlit-predict`
+]
+logging = [
+ "comet", # https://docs.ultralytics.com/integrations/comet/
+ "tensorboard>=2.13.0",
+ "dvclive>=2.12.0",
+]
+extra = [
+ "hub-sdk>=0.0.12", # Ultralytics HUB
+ "ipython", # interactive notebook
+ "albumentations>=1.4.6", # training augmentations
+ "pycocotools>=2.0.7", # COCO mAP
+]
+
+[project.urls]
+"Homepage" = "https://ultralytics.com"
+"Source" = "https://github.com/ultralytics/ultralytics"
+"Documentation" = "https://docs.ultralytics.com"
+"Bug Reports" = "https://github.com/ultralytics/ultralytics/issues"
+"Changelog" = "https://github.com/ultralytics/ultralytics/releases"
+
+[project.scripts]
+yolo = "ultralytics.cfg:entrypoint"
+ultralytics = "ultralytics.cfg:entrypoint"
+
+# Tools settings -------------------------------------------------------------------------------------------------------
+[tool.setuptools] # configuration specific to the `setuptools` build backend.
+packages = { find = { where = ["."], include = ["ultralytics", "ultralytics.*"] } }
+package-data = { "ultralytics" = ["**/*.yaml", "../tests/*.py"], "ultralytics.assets" = ["*.jpg"] }
+
+[tool.setuptools.dynamic]
+version = { attr = "ultralytics.__version__" }
+
+[tool.pytest.ini_options]
+addopts = "--doctest-modules --durations=30 --color=yes"
+markers = [
+ "slow: skip slow tests unless --slow is set",
+]
+norecursedirs = [".git", "dist", "build"]
+
+[tool.coverage.run]
+source = ["ultralytics/"]
+data_file = "tests/.coverage"
+omit = ["ultralytics/utils/callbacks/*"]
+
+[tool.isort]
+line_length = 120
+multi_line_output = 0
+
+[tool.yapf]
+based_on_style = "pep8"
+spaces_before_comment = 2
+column_limit = 120
+coalesce_brackets = true
+spaces_around_power_operator = true
+space_between_ending_comma_and_closing_bracket = true
+split_before_closing_bracket = false
+split_before_first_argument = false
+
+[tool.ruff]
+line-length = 120
+
+[tool.ruff.format]
+docstring-code-format = true
+
+[tool.docformatter]
+wrap-summaries = 120
+wrap-descriptions = 120
+pre-summary-newline = true
+close-quotes-on-newline = true
+in-place = true
+
+[tool.codespell]
+ignore-words-list = "crate,nd,ned,strack,dota,ane,segway,fo,gool,winn,commend,bloc,nam,afterall"
+skip = '*.pt,*.pth,*.torchscript,*.onnx,*.tflite,*.pb,*.bin,*.param,*.mlmodel,*.engine,*.npy,*.data*,*.csv,*pnnx*,*venv*,*translat*,__pycache__*,*.ico,*.jpg,*.png,*.mp4,*.mov,/runs,/.git,./docs/??/*.md,./docs/mkdocs_??.yml'
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..38e813d20d8306e6dbbb2eb9e4722bd7d3e5a292
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,19 @@
+torch==2.2.2
+torchvision==0.17.2
+flash_attn-2.7.3+cu11torch2.2cxx11abiFALSE-cp311-cp311-linux_x86_64.whl
+timm==1.0.14
+albumentations==2.0.4
+onnx==1.14.0
+onnxruntime==1.15.1
+pycocotools==2.0.7
+PyYAML==6.0.1
+scipy==1.13.0
+onnxslim==0.1.31
+onnxruntime-gpu==1.18.0
+gradio==4.44.1
+opencv-python==4.9.0.80
+psutil==5.9.8
+py-cpuinfo==9.0.0
+huggingface-hub==0.23.2
+safetensors==0.4.3
+numpy==1.26.4
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9e86aa3c593d0def794a67a5eeadbbf46bba3097
--- /dev/null
+++ b/tests/__init__.py
@@ -0,0 +1,22 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from ultralytics.utils import ASSETS, ROOT, WEIGHTS_DIR, checks
+
+# Constants used in tests
+MODEL = WEIGHTS_DIR / "path with spaces" / "yolo11n.pt" # test spaces in path
+CFG = "yolo11n.yaml"
+SOURCE = ASSETS / "bus.jpg"
+SOURCES_LIST = [ASSETS / "bus.jpg", ASSETS, ASSETS / "*", ASSETS / "**/*.jpg"]
+TMP = (ROOT / "../tests/tmp").resolve() # temp directory for test files
+CUDA_IS_AVAILABLE = checks.cuda_is_available()
+CUDA_DEVICE_COUNT = checks.cuda_device_count()
+
+__all__ = (
+ "MODEL",
+ "CFG",
+ "SOURCE",
+ "SOURCES_LIST",
+ "TMP",
+ "CUDA_IS_AVAILABLE",
+ "CUDA_DEVICE_COUNT",
+)
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000000000000000000000000000000000000..8703d81fce7a50a1a182fb89c5098d88d321f263
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,83 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import shutil
+from pathlib import Path
+
+from tests import TMP
+
+
+def pytest_addoption(parser):
+ """
+ Add custom command-line options to pytest.
+
+ Args:
+ parser (pytest.config.Parser): The pytest parser object for adding custom command-line options.
+
+ Returns:
+ (None)
+ """
+ parser.addoption("--slow", action="store_true", default=False, help="Run slow tests")
+
+
+def pytest_collection_modifyitems(config, items):
+ """
+ Modify the list of test items to exclude tests marked as slow if the --slow option is not specified.
+
+ Args:
+ config (pytest.config.Config): The pytest configuration object that provides access to command-line options.
+ items (list): The list of collected pytest item objects to be modified based on the presence of --slow option.
+
+ Returns:
+ (None) The function modifies the 'items' list in place, and does not return a value.
+ """
+ if not config.getoption("--slow"):
+ # Remove the item entirely from the list of test items if it's marked as 'slow'
+ items[:] = [item for item in items if "slow" not in item.keywords]
+
+
+def pytest_sessionstart(session):
+ """
+ Initialize session configurations for pytest.
+
+ This function is automatically called by pytest after the 'Session' object has been created but before performing
+ test collection. It sets the initial seeds and prepares the temporary directory for the test session.
+
+ Args:
+ session (pytest.Session): The pytest session object.
+
+ Returns:
+ (None)
+ """
+ from ultralytics.utils.torch_utils import init_seeds
+
+ init_seeds()
+ shutil.rmtree(TMP, ignore_errors=True) # delete any existing tests/tmp directory
+ TMP.mkdir(parents=True, exist_ok=True) # create a new empty directory
+
+
+def pytest_terminal_summary(terminalreporter, exitstatus, config):
+ """
+ Cleanup operations after pytest session.
+
+ This function is automatically called by pytest at the end of the entire test session. It removes certain files
+ and directories used during testing.
+
+ Args:
+ terminalreporter (pytest.terminal.TerminalReporter): The terminal reporter object used for terminal output.
+ exitstatus (int): The exit status of the test run.
+ config (pytest.config.Config): The pytest config object.
+
+ Returns:
+ (None)
+ """
+ from ultralytics.utils import WEIGHTS_DIR
+
+ # Remove files
+ models = [path for x in ["*.onnx", "*.torchscript"] for path in WEIGHTS_DIR.rglob(x)]
+ for file in ["decelera_portrait_min.mov", "bus.jpg", "yolo11n.onnx", "yolo11n.torchscript"] + models:
+ Path(file).unlink(missing_ok=True)
+
+ # Remove directories
+ models = [path for x in ["*.mlpackage", "*_openvino_model"] for path in WEIGHTS_DIR.rglob(x)]
+ for directory in [WEIGHTS_DIR / "path with spaces", TMP.parents[1] / ".pytest_cache", TMP] + models:
+ shutil.rmtree(directory, ignore_errors=True)
diff --git a/tests/test_cli.py b/tests/test_cli.py
new file mode 100644
index 0000000000000000000000000000000000000000..aab6d8b4ac7d2dcd5a903e10a355fcb1c1449377
--- /dev/null
+++ b/tests/test_cli.py
@@ -0,0 +1,122 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import subprocess
+
+import pytest
+from PIL import Image
+
+from tests import CUDA_DEVICE_COUNT, CUDA_IS_AVAILABLE
+from ultralytics.cfg import TASK2DATA, TASK2MODEL, TASKS
+from ultralytics.utils import ASSETS, WEIGHTS_DIR, checks
+from ultralytics.utils.torch_utils import TORCH_1_9
+
+# Constants
+TASK_MODEL_DATA = [(task, WEIGHTS_DIR / TASK2MODEL[task], TASK2DATA[task]) for task in TASKS]
+MODELS = [WEIGHTS_DIR / TASK2MODEL[task] for task in TASKS]
+
+
+def run(cmd):
+ """Execute a shell command using subprocess."""
+ subprocess.run(cmd.split(), check=True)
+
+
+def test_special_modes():
+ """Test various special command-line modes for YOLO functionality."""
+ run("yolo help")
+ run("yolo checks")
+ run("yolo version")
+ run("yolo settings reset")
+ run("yolo cfg")
+
+
+@pytest.mark.parametrize("task,model,data", TASK_MODEL_DATA)
+def test_train(task, model, data):
+ """Test YOLO training for different tasks, models, and datasets."""
+ run(f"yolo train {task} model={model} data={data} imgsz=32 epochs=1 cache=disk")
+
+
+@pytest.mark.parametrize("task,model,data", TASK_MODEL_DATA)
+def test_val(task, model, data):
+ """Test YOLO validation process for specified task, model, and data using a shell command."""
+ run(f"yolo val {task} model={model} data={data} imgsz=32 save_txt save_json")
+
+
+@pytest.mark.parametrize("task,model,data", TASK_MODEL_DATA)
+def test_predict(task, model, data):
+ """Test YOLO prediction on provided sample assets for specified task and model."""
+ run(f"yolo predict model={model} source={ASSETS} imgsz=32 save save_crop save_txt")
+
+
+@pytest.mark.parametrize("model", MODELS)
+def test_export(model):
+ """Test exporting a YOLO model to TorchScript format."""
+ run(f"yolo export model={model} format=torchscript imgsz=32")
+
+
+def test_rtdetr(task="detect", model="yolov8n-rtdetr.yaml", data="coco8.yaml"):
+ """Test the RTDETR functionality within Ultralytics for detection tasks using specified model and data."""
+ # Warning: must use imgsz=640 (note also add coma, spaces, fraction=0.25 args to test single-image training)
+ run(f"yolo train {task} model={model} data={data} --imgsz= 160 epochs =1, cache = disk fraction=0.25")
+ run(f"yolo predict {task} model={model} source={ASSETS / 'bus.jpg'} imgsz=160 save save_crop save_txt")
+ if TORCH_1_9:
+ weights = WEIGHTS_DIR / "rtdetr-l.pt"
+ run(f"yolo predict {task} model={weights} source={ASSETS / 'bus.jpg'} imgsz=160 save save_crop save_txt")
+
+
+@pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="MobileSAM with CLIP is not supported in Python 3.12")
+def test_fastsam(task="segment", model=WEIGHTS_DIR / "FastSAM-s.pt", data="coco8-seg.yaml"):
+ """Test FastSAM model for segmenting objects in images using various prompts within Ultralytics."""
+ source = ASSETS / "bus.jpg"
+
+ run(f"yolo segment val {task} model={model} data={data} imgsz=32")
+ run(f"yolo segment predict model={model} source={source} imgsz=32 save save_crop save_txt")
+
+ from ultralytics import FastSAM
+ from ultralytics.models.sam import Predictor
+
+ # Create a FastSAM model
+ sam_model = FastSAM(model) # or FastSAM-x.pt
+
+ # Run inference on an image
+ for s in (source, Image.open(source)):
+ everything_results = sam_model(s, device="cpu", retina_masks=True, imgsz=320, conf=0.4, iou=0.9)
+
+ # Remove small regions
+ new_masks, _ = Predictor.remove_small_regions(everything_results[0].masks.data, min_area=20)
+
+ # Run inference with bboxes and points and texts prompt at the same time
+ sam_model(source, bboxes=[439, 437, 524, 709], points=[[200, 200]], labels=[1], texts="a photo of a dog")
+
+
+def test_mobilesam():
+ """Test MobileSAM segmentation with point prompts using Ultralytics."""
+ from ultralytics import SAM
+
+ # Load the model
+ model = SAM(WEIGHTS_DIR / "mobile_sam.pt")
+
+ # Source
+ source = ASSETS / "zidane.jpg"
+
+ # Predict a segment based on a 1D point prompt and 1D labels.
+ model.predict(source, points=[900, 370], labels=[1])
+
+ # Predict a segment based on 3D points and 2D labels (multiple points per object).
+ model.predict(source, points=[[[900, 370], [1000, 100]]], labels=[[1, 1]])
+
+ # Predict a segment based on a box prompt
+ model.predict(source, bboxes=[439, 437, 524, 709], save=True)
+
+ # Predict all
+ # model(source)
+
+
+# Slow Tests -----------------------------------------------------------------------------------------------------------
+@pytest.mark.slow
+@pytest.mark.parametrize("task,model,data", TASK_MODEL_DATA)
+@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available")
+@pytest.mark.skipif(CUDA_DEVICE_COUNT < 2, reason="DDP is not available")
+def test_train_gpu(task, model, data):
+ """Test YOLO training on GPU(s) for various tasks and models."""
+ run(f"yolo train {task} model={model} data={data} imgsz=32 epochs=1 device=0") # single GPU
+ run(f"yolo train {task} model={model} data={data} imgsz=32 epochs=1 device=0,1") # multi GPU
diff --git a/tests/test_cuda.py b/tests/test_cuda.py
new file mode 100644
index 0000000000000000000000000000000000000000..d94f95bd06b7efd61189d310537f3298d384024f
--- /dev/null
+++ b/tests/test_cuda.py
@@ -0,0 +1,155 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from itertools import product
+from pathlib import Path
+
+import pytest
+import torch
+
+from tests import CUDA_DEVICE_COUNT, CUDA_IS_AVAILABLE, MODEL, SOURCE
+from ultralytics import YOLO
+from ultralytics.cfg import TASK2DATA, TASK2MODEL, TASKS
+from ultralytics.utils import ASSETS, WEIGHTS_DIR
+from ultralytics.utils.checks import check_amp
+
+
+def test_checks():
+ """Validate CUDA settings against torch CUDA functions."""
+ assert torch.cuda.is_available() == CUDA_IS_AVAILABLE
+ assert torch.cuda.device_count() == CUDA_DEVICE_COUNT
+
+
+@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available")
+def test_amp():
+ """Test AMP training checks."""
+ model = YOLO("yolo11n.pt").model.cuda()
+ assert check_amp(model)
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(True, reason="CUDA export tests disabled pending additional Ultralytics GPU server availability")
+@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available")
+@pytest.mark.parametrize(
+ "task, dynamic, int8, half, batch",
+ [ # generate all combinations but exclude those where both int8 and half are True
+ (task, dynamic, int8, half, batch)
+ # Note: tests reduced below pending compute availability expansion as GPU CI runner utilization is high
+ # for task, dynamic, int8, half, batch in product(TASKS, [True, False], [True, False], [True, False], [1, 2])
+ for task, dynamic, int8, half, batch in product(TASKS, [True], [True], [False], [2])
+ if not (int8 and half) # exclude cases where both int8 and half are True
+ ],
+)
+def test_export_engine_matrix(task, dynamic, int8, half, batch):
+ """Test YOLO model export to TensorRT format for various configurations and run inference."""
+ file = YOLO(TASK2MODEL[task]).export(
+ format="engine",
+ imgsz=32,
+ dynamic=dynamic,
+ int8=int8,
+ half=half,
+ batch=batch,
+ data=TASK2DATA[task],
+ workspace=1, # reduce workspace GB for less resource utilization during testing
+ simplify=True, # use 'onnxslim'
+ )
+ YOLO(file)([SOURCE] * batch, imgsz=64 if dynamic else 32) # exported model inference
+ Path(file).unlink() # cleanup
+ Path(file).with_suffix(".cache").unlink() if int8 else None # cleanup INT8 cache
+
+
+@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available")
+def test_train():
+ """Test model training on a minimal dataset using available CUDA devices."""
+ device = 0 if CUDA_DEVICE_COUNT == 1 else [0, 1]
+ YOLO(MODEL).train(data="coco8.yaml", imgsz=64, epochs=1, device=device) # requires imgsz>=64
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available")
+def test_predict_multiple_devices():
+ """Validate model prediction consistency across CPU and CUDA devices."""
+ model = YOLO("yolo11n.pt")
+ model = model.cpu()
+ assert str(model.device) == "cpu"
+ _ = model(SOURCE) # CPU inference
+ assert str(model.device) == "cpu"
+
+ model = model.to("cuda:0")
+ assert str(model.device) == "cuda:0"
+ _ = model(SOURCE) # CUDA inference
+ assert str(model.device) == "cuda:0"
+
+ model = model.cpu()
+ assert str(model.device) == "cpu"
+ _ = model(SOURCE) # CPU inference
+ assert str(model.device) == "cpu"
+
+ model = model.cuda()
+ assert str(model.device) == "cuda:0"
+ _ = model(SOURCE) # CUDA inference
+ assert str(model.device) == "cuda:0"
+
+
+@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available")
+def test_autobatch():
+ """Check optimal batch size for YOLO model training using autobatch utility."""
+ from ultralytics.utils.autobatch import check_train_batch_size
+
+ check_train_batch_size(YOLO(MODEL).model.cuda(), imgsz=128, amp=True)
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available")
+def test_utils_benchmarks():
+ """Profile YOLO models for performance benchmarks."""
+ from ultralytics.utils.benchmarks import ProfileModels
+
+ # Pre-export a dynamic engine model to use dynamic inference
+ YOLO(MODEL).export(format="engine", imgsz=32, dynamic=True, batch=1)
+ ProfileModels([MODEL], imgsz=32, half=False, min_time=1, num_timed_runs=3, num_warmup_runs=1).profile()
+
+
+@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available")
+def test_predict_sam():
+ """Test SAM model predictions using different prompts, including bounding boxes and point annotations."""
+ from ultralytics import SAM
+ from ultralytics.models.sam import Predictor as SAMPredictor
+
+ # Load a model
+ model = SAM(WEIGHTS_DIR / "sam2.1_b.pt")
+
+ # Display model information (optional)
+ model.info()
+
+ # Run inference
+ model(SOURCE, device=0)
+
+ # Run inference with bboxes prompt
+ model(SOURCE, bboxes=[439, 437, 524, 709], device=0)
+
+ # Run inference with no labels
+ model(ASSETS / "zidane.jpg", points=[900, 370], device=0)
+
+ # Run inference with 1D points and 1D labels
+ model(ASSETS / "zidane.jpg", points=[900, 370], labels=[1], device=0)
+
+ # Run inference with 2D points and 1D labels
+ model(ASSETS / "zidane.jpg", points=[[900, 370]], labels=[1], device=0)
+
+ # Run inference with multiple 2D points and 1D labels
+ model(ASSETS / "zidane.jpg", points=[[400, 370], [900, 370]], labels=[1, 1], device=0)
+
+ # Run inference with 3D points and 2D labels (multiple points per object)
+ model(ASSETS / "zidane.jpg", points=[[[900, 370], [1000, 100]]], labels=[[1, 1]], device=0)
+
+ # Create SAMPredictor
+ overrides = dict(conf=0.25, task="segment", mode="predict", imgsz=1024, model=WEIGHTS_DIR / "mobile_sam.pt")
+ predictor = SAMPredictor(overrides=overrides)
+
+ # Set image
+ predictor.set_image(ASSETS / "zidane.jpg") # set with image file
+ # predictor(bboxes=[439, 437, 524, 709])
+ # predictor(points=[900, 370], labels=[1])
+
+ # Reset image
+ predictor.reset_image()
diff --git a/tests/test_engine.py b/tests/test_engine.py
new file mode 100644
index 0000000000000000000000000000000000000000..fe95a5ca5dd557cb9fc82ff2e61755d7bcd6e81b
--- /dev/null
+++ b/tests/test_engine.py
@@ -0,0 +1,131 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import sys
+from unittest import mock
+
+from tests import MODEL
+from ultralytics import YOLO
+from ultralytics.cfg import get_cfg
+from ultralytics.engine.exporter import Exporter
+from ultralytics.models.yolo import classify, detect, segment
+from ultralytics.utils import ASSETS, DEFAULT_CFG, WEIGHTS_DIR
+
+
+def test_func(*args): # noqa
+ """Test function callback for evaluating YOLO model performance metrics."""
+ print("callback test passed")
+
+
+def test_export():
+ """Tests the model exporting function by adding a callback and asserting its execution."""
+ exporter = Exporter()
+ exporter.add_callback("on_export_start", test_func)
+ assert test_func in exporter.callbacks["on_export_start"], "callback test failed"
+ f = exporter(model=YOLO("yolo11n.yaml").model)
+ YOLO(f)(ASSETS) # exported model inference
+
+
+def test_detect():
+ """Test YOLO object detection training, validation, and prediction functionality."""
+ overrides = {"data": "coco8.yaml", "model": "yolo11n.yaml", "imgsz": 32, "epochs": 1, "save": False}
+ cfg = get_cfg(DEFAULT_CFG)
+ cfg.data = "coco8.yaml"
+ cfg.imgsz = 32
+
+ # Trainer
+ trainer = detect.DetectionTrainer(overrides=overrides)
+ trainer.add_callback("on_train_start", test_func)
+ assert test_func in trainer.callbacks["on_train_start"], "callback test failed"
+ trainer.train()
+
+ # Validator
+ val = detect.DetectionValidator(args=cfg)
+ val.add_callback("on_val_start", test_func)
+ assert test_func in val.callbacks["on_val_start"], "callback test failed"
+ val(model=trainer.best) # validate best.pt
+
+ # Predictor
+ pred = detect.DetectionPredictor(overrides={"imgsz": [64, 64]})
+ pred.add_callback("on_predict_start", test_func)
+ assert test_func in pred.callbacks["on_predict_start"], "callback test failed"
+ # Confirm there is no issue with sys.argv being empty.
+ with mock.patch.object(sys, "argv", []):
+ result = pred(source=ASSETS, model=MODEL)
+ assert len(result), "predictor test failed"
+
+ overrides["resume"] = trainer.last
+ trainer = detect.DetectionTrainer(overrides=overrides)
+ try:
+ trainer.train()
+ except Exception as e:
+ print(f"Expected exception caught: {e}")
+ return
+
+ Exception("Resume test failed!")
+
+
+def test_segment():
+ """Tests image segmentation training, validation, and prediction pipelines using YOLO models."""
+ overrides = {"data": "coco8-seg.yaml", "model": "yolo11n-seg.yaml", "imgsz": 32, "epochs": 1, "save": False}
+ cfg = get_cfg(DEFAULT_CFG)
+ cfg.data = "coco8-seg.yaml"
+ cfg.imgsz = 32
+ # YOLO(CFG_SEG).train(**overrides) # works
+
+ # Trainer
+ trainer = segment.SegmentationTrainer(overrides=overrides)
+ trainer.add_callback("on_train_start", test_func)
+ assert test_func in trainer.callbacks["on_train_start"], "callback test failed"
+ trainer.train()
+
+ # Validator
+ val = segment.SegmentationValidator(args=cfg)
+ val.add_callback("on_val_start", test_func)
+ assert test_func in val.callbacks["on_val_start"], "callback test failed"
+ val(model=trainer.best) # validate best.pt
+
+ # Predictor
+ pred = segment.SegmentationPredictor(overrides={"imgsz": [64, 64]})
+ pred.add_callback("on_predict_start", test_func)
+ assert test_func in pred.callbacks["on_predict_start"], "callback test failed"
+ result = pred(source=ASSETS, model=WEIGHTS_DIR / "yolo11n-seg.pt")
+ assert len(result), "predictor test failed"
+
+ # Test resume
+ overrides["resume"] = trainer.last
+ trainer = segment.SegmentationTrainer(overrides=overrides)
+ try:
+ trainer.train()
+ except Exception as e:
+ print(f"Expected exception caught: {e}")
+ return
+
+ Exception("Resume test failed!")
+
+
+def test_classify():
+ """Test image classification including training, validation, and prediction phases."""
+ overrides = {"data": "imagenet10", "model": "yolo11n-cls.yaml", "imgsz": 32, "epochs": 1, "save": False}
+ cfg = get_cfg(DEFAULT_CFG)
+ cfg.data = "imagenet10"
+ cfg.imgsz = 32
+ # YOLO(CFG_SEG).train(**overrides) # works
+
+ # Trainer
+ trainer = classify.ClassificationTrainer(overrides=overrides)
+ trainer.add_callback("on_train_start", test_func)
+ assert test_func in trainer.callbacks["on_train_start"], "callback test failed"
+ trainer.train()
+
+ # Validator
+ val = classify.ClassificationValidator(args=cfg)
+ val.add_callback("on_val_start", test_func)
+ assert test_func in val.callbacks["on_val_start"], "callback test failed"
+ val(model=trainer.best)
+
+ # Predictor
+ pred = classify.ClassificationPredictor(overrides={"imgsz": [64, 64]})
+ pred.add_callback("on_predict_start", test_func)
+ assert test_func in pred.callbacks["on_predict_start"], "callback test failed"
+ result = pred(source=ASSETS, model=trainer.best)
+ assert len(result), "predictor test failed"
diff --git a/tests/test_exports.py b/tests/test_exports.py
new file mode 100644
index 0000000000000000000000000000000000000000..34dc32de1bc6e2ca220ec880bc5351fc07e02a23
--- /dev/null
+++ b/tests/test_exports.py
@@ -0,0 +1,216 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import shutil
+import uuid
+from itertools import product
+from pathlib import Path
+
+import pytest
+
+from tests import MODEL, SOURCE
+from ultralytics import YOLO
+from ultralytics.cfg import TASK2DATA, TASK2MODEL, TASKS
+from ultralytics.utils import (
+ IS_RASPBERRYPI,
+ LINUX,
+ MACOS,
+ WINDOWS,
+ checks,
+)
+from ultralytics.utils.torch_utils import TORCH_1_9, TORCH_1_13
+
+
+def test_export_torchscript():
+ """Test YOLO model exporting to TorchScript format for compatibility and correctness."""
+ file = YOLO(MODEL).export(format="torchscript", optimize=False, imgsz=32)
+ YOLO(file)(SOURCE, imgsz=32) # exported model inference
+
+
+def test_export_onnx():
+ """Test YOLO model export to ONNX format with dynamic axes."""
+ file = YOLO(MODEL).export(format="onnx", dynamic=True, imgsz=32)
+ YOLO(file)(SOURCE, imgsz=32) # exported model inference
+
+
+@pytest.mark.skipif(not TORCH_1_13, reason="OpenVINO requires torch>=1.13")
+def test_export_openvino():
+ """Test YOLO exports to OpenVINO format for model inference compatibility."""
+ file = YOLO(MODEL).export(format="openvino", imgsz=32)
+ YOLO(file)(SOURCE, imgsz=32) # exported model inference
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(not TORCH_1_13, reason="OpenVINO requires torch>=1.13")
+@pytest.mark.parametrize(
+ "task, dynamic, int8, half, batch",
+ [ # generate all combinations but exclude those where both int8 and half are True
+ (task, dynamic, int8, half, batch)
+ for task, dynamic, int8, half, batch in product(TASKS, [True, False], [True, False], [True, False], [1, 2])
+ if not (int8 and half) # exclude cases where both int8 and half are True
+ ],
+)
+def test_export_openvino_matrix(task, dynamic, int8, half, batch):
+ """Test YOLO model exports to OpenVINO under various configuration matrix conditions."""
+ file = YOLO(TASK2MODEL[task]).export(
+ format="openvino",
+ imgsz=32,
+ dynamic=dynamic,
+ int8=int8,
+ half=half,
+ batch=batch,
+ data=TASK2DATA[task],
+ )
+ if WINDOWS:
+ # Use unique filenames due to Windows file permissions bug possibly due to latent threaded use
+ # See https://github.com/ultralytics/ultralytics/actions/runs/8957949304/job/24601616830?pr=10423
+ file = Path(file)
+ file = file.rename(file.with_stem(f"{file.stem}-{uuid.uuid4()}"))
+ YOLO(file)([SOURCE] * batch, imgsz=64 if dynamic else 32) # exported model inference
+ shutil.rmtree(file, ignore_errors=True) # retry in case of potential lingering multi-threaded file usage errors
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize(
+ "task, dynamic, int8, half, batch, simplify", product(TASKS, [True, False], [False], [False], [1, 2], [True, False])
+)
+def test_export_onnx_matrix(task, dynamic, int8, half, batch, simplify):
+ """Test YOLO exports to ONNX format with various configurations and parameters."""
+ file = YOLO(TASK2MODEL[task]).export(
+ format="onnx",
+ imgsz=32,
+ dynamic=dynamic,
+ int8=int8,
+ half=half,
+ batch=batch,
+ simplify=simplify,
+ )
+ YOLO(file)([SOURCE] * batch, imgsz=64 if dynamic else 32) # exported model inference
+ Path(file).unlink() # cleanup
+
+
+@pytest.mark.slow
+@pytest.mark.parametrize("task, dynamic, int8, half, batch", product(TASKS, [False], [False], [False], [1, 2]))
+def test_export_torchscript_matrix(task, dynamic, int8, half, batch):
+ """Tests YOLO model exports to TorchScript format under varied configurations."""
+ file = YOLO(TASK2MODEL[task]).export(
+ format="torchscript",
+ imgsz=32,
+ dynamic=dynamic,
+ int8=int8,
+ half=half,
+ batch=batch,
+ )
+ YOLO(file)([SOURCE] * 3, imgsz=64 if dynamic else 32) # exported model inference at batch=3
+ Path(file).unlink() # cleanup
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(not MACOS, reason="CoreML inference only supported on macOS")
+@pytest.mark.skipif(not TORCH_1_9, reason="CoreML>=7.2 not supported with PyTorch<=1.8")
+@pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="CoreML not supported in Python 3.12")
+@pytest.mark.parametrize(
+ "task, dynamic, int8, half, batch",
+ [ # generate all combinations but exclude those where both int8 and half are True
+ (task, dynamic, int8, half, batch)
+ for task, dynamic, int8, half, batch in product(TASKS, [False], [True, False], [True, False], [1])
+ if not (int8 and half) # exclude cases where both int8 and half are True
+ ],
+)
+def test_export_coreml_matrix(task, dynamic, int8, half, batch):
+ """Test YOLO exports to CoreML format with various parameter configurations."""
+ file = YOLO(TASK2MODEL[task]).export(
+ format="coreml",
+ imgsz=32,
+ dynamic=dynamic,
+ int8=int8,
+ half=half,
+ batch=batch,
+ )
+ YOLO(file)([SOURCE] * batch, imgsz=32) # exported model inference at batch=3
+ shutil.rmtree(file) # cleanup
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(not checks.IS_PYTHON_MINIMUM_3_10, reason="TFLite export requires Python>=3.10")
+@pytest.mark.skipif(not LINUX, reason="Test disabled as TF suffers from install conflicts on Windows and macOS")
+@pytest.mark.parametrize(
+ "task, dynamic, int8, half, batch",
+ [ # generate all combinations but exclude those where both int8 and half are True
+ (task, dynamic, int8, half, batch)
+ for task, dynamic, int8, half, batch in product(TASKS, [False], [True, False], [True, False], [1])
+ if not (int8 and half) # exclude cases where both int8 and half are True
+ ],
+)
+def test_export_tflite_matrix(task, dynamic, int8, half, batch):
+ """Test YOLO exports to TFLite format considering various export configurations."""
+ file = YOLO(TASK2MODEL[task]).export(
+ format="tflite",
+ imgsz=32,
+ dynamic=dynamic,
+ int8=int8,
+ half=half,
+ batch=batch,
+ )
+ YOLO(file)([SOURCE] * batch, imgsz=32) # exported model inference at batch=3
+ Path(file).unlink() # cleanup
+
+
+@pytest.mark.skipif(not TORCH_1_9, reason="CoreML>=7.2 not supported with PyTorch<=1.8")
+@pytest.mark.skipif(WINDOWS, reason="CoreML not supported on Windows") # RuntimeError: BlobWriter not loaded
+@pytest.mark.skipif(IS_RASPBERRYPI, reason="CoreML not supported on Raspberry Pi")
+@pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="CoreML not supported in Python 3.12")
+def test_export_coreml():
+ """Test YOLO exports to CoreML format, optimized for macOS only."""
+ if MACOS:
+ file = YOLO(MODEL).export(format="coreml", imgsz=32)
+ YOLO(file)(SOURCE, imgsz=32) # model prediction only supported on macOS for nms=False models
+ else:
+ YOLO(MODEL).export(format="coreml", nms=True, imgsz=32)
+
+
+@pytest.mark.skipif(not checks.IS_PYTHON_MINIMUM_3_10, reason="TFLite export requires Python>=3.10")
+@pytest.mark.skipif(not LINUX, reason="Test disabled as TF suffers from install conflicts on Windows and macOS")
+def test_export_tflite():
+ """Test YOLO exports to TFLite format under specific OS and Python version conditions."""
+ model = YOLO(MODEL)
+ file = model.export(format="tflite", imgsz=32)
+ YOLO(file)(SOURCE, imgsz=32)
+
+
+@pytest.mark.skipif(True, reason="Test disabled")
+@pytest.mark.skipif(not LINUX, reason="TF suffers from install conflicts on Windows and macOS")
+def test_export_pb():
+ """Test YOLO exports to TensorFlow's Protobuf (*.pb) format."""
+ model = YOLO(MODEL)
+ file = model.export(format="pb", imgsz=32)
+ YOLO(file)(SOURCE, imgsz=32)
+
+
+@pytest.mark.skipif(True, reason="Test disabled as Paddle protobuf and ONNX protobuf requirements conflict.")
+def test_export_paddle():
+ """Test YOLO exports to Paddle format, noting protobuf conflicts with ONNX."""
+ YOLO(MODEL).export(format="paddle", imgsz=32)
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(IS_RASPBERRYPI, reason="MNN not supported on Raspberry Pi")
+def test_export_mnn():
+ """Test YOLO exports to MNN format (WARNING: MNN test must precede NCNN test or CI error on Windows)."""
+ file = YOLO(MODEL).export(format="mnn", imgsz=32)
+ YOLO(file)(SOURCE, imgsz=32) # exported model inference
+
+
+@pytest.mark.slow
+def test_export_ncnn():
+ """Test YOLO exports to NCNN format."""
+ file = YOLO(MODEL).export(format="ncnn", imgsz=32)
+ YOLO(file)(SOURCE, imgsz=32) # exported model inference
+
+
+@pytest.mark.skipif(True, reason="Test disabled as keras and tensorflow version conflicts with tflite export.")
+@pytest.mark.skipif(not LINUX or MACOS, reason="Skipping test on Windows and Macos")
+def test_export_imx():
+ """Test YOLOv8n exports to IMX format."""
+ model = YOLO("yolov8n.pt")
+ file = model.export(format="imx", imgsz=32)
+ YOLO(file)(SOURCE, imgsz=32)
diff --git a/tests/test_integrations.py b/tests/test_integrations.py
new file mode 100644
index 0000000000000000000000000000000000000000..8067a1787f142f0f3d8f0834713ee6709d671115
--- /dev/null
+++ b/tests/test_integrations.py
@@ -0,0 +1,150 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import contextlib
+import os
+import subprocess
+import time
+from pathlib import Path
+
+import pytest
+
+from tests import MODEL, SOURCE, TMP
+from ultralytics import YOLO, download
+from ultralytics.utils import DATASETS_DIR, SETTINGS
+from ultralytics.utils.checks import check_requirements
+
+
+@pytest.mark.skipif(not check_requirements("ray", install=False), reason="ray[tune] not installed")
+def test_model_ray_tune():
+ """Tune YOLO model using Ray for hyperparameter optimization."""
+ YOLO("yolo11n-cls.yaml").tune(
+ use_ray=True, data="imagenet10", grace_period=1, iterations=1, imgsz=32, epochs=1, plots=False, device="cpu"
+ )
+
+
+@pytest.mark.skipif(not check_requirements("mlflow", install=False), reason="mlflow not installed")
+def test_mlflow():
+ """Test training with MLflow tracking enabled (see https://mlflow.org/ for details)."""
+ SETTINGS["mlflow"] = True
+ YOLO("yolo11n-cls.yaml").train(data="imagenet10", imgsz=32, epochs=3, plots=False, device="cpu")
+ SETTINGS["mlflow"] = False
+
+
+@pytest.mark.skipif(True, reason="Test failing in scheduled CI https://github.com/ultralytics/ultralytics/pull/8868")
+@pytest.mark.skipif(not check_requirements("mlflow", install=False), reason="mlflow not installed")
+def test_mlflow_keep_run_active():
+ """Ensure MLflow run status matches MLFLOW_KEEP_RUN_ACTIVE environment variable settings."""
+ import mlflow
+
+ SETTINGS["mlflow"] = True
+ run_name = "Test Run"
+ os.environ["MLFLOW_RUN"] = run_name
+
+ # Test with MLFLOW_KEEP_RUN_ACTIVE=True
+ os.environ["MLFLOW_KEEP_RUN_ACTIVE"] = "True"
+ YOLO("yolo11n-cls.yaml").train(data="imagenet10", imgsz=32, epochs=1, plots=False, device="cpu")
+ status = mlflow.active_run().info.status
+ assert status == "RUNNING", "MLflow run should be active when MLFLOW_KEEP_RUN_ACTIVE=True"
+
+ run_id = mlflow.active_run().info.run_id
+
+ # Test with MLFLOW_KEEP_RUN_ACTIVE=False
+ os.environ["MLFLOW_KEEP_RUN_ACTIVE"] = "False"
+ YOLO("yolo11n-cls.yaml").train(data="imagenet10", imgsz=32, epochs=1, plots=False, device="cpu")
+ status = mlflow.get_run(run_id=run_id).info.status
+ assert status == "FINISHED", "MLflow run should be ended when MLFLOW_KEEP_RUN_ACTIVE=False"
+
+ # Test with MLFLOW_KEEP_RUN_ACTIVE not set
+ os.environ.pop("MLFLOW_KEEP_RUN_ACTIVE", None)
+ YOLO("yolo11n-cls.yaml").train(data="imagenet10", imgsz=32, epochs=1, plots=False, device="cpu")
+ status = mlflow.get_run(run_id=run_id).info.status
+ assert status == "FINISHED", "MLflow run should be ended by default when MLFLOW_KEEP_RUN_ACTIVE is not set"
+ SETTINGS["mlflow"] = False
+
+
+@pytest.mark.skipif(not check_requirements("tritonclient", install=False), reason="tritonclient[all] not installed")
+def test_triton():
+ """
+ Test NVIDIA Triton Server functionalities with YOLO model.
+
+ See https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver.
+ """
+ check_requirements("tritonclient[all]")
+ from tritonclient.http import InferenceServerClient # noqa
+
+ # Create variables
+ model_name = "yolo"
+ triton_repo = TMP / "triton_repo" # Triton repo path
+ triton_model = triton_repo / model_name # Triton model path
+
+ # Export model to ONNX
+ f = YOLO(MODEL).export(format="onnx", dynamic=True)
+
+ # Prepare Triton repo
+ (triton_model / "1").mkdir(parents=True, exist_ok=True)
+ Path(f).rename(triton_model / "1" / "model.onnx")
+ (triton_model / "config.pbtxt").touch()
+
+ # Define image https://catalog.ngc.nvidia.com/orgs/nvidia/containers/tritonserver
+ tag = "nvcr.io/nvidia/tritonserver:23.09-py3" # 6.4 GB
+
+ # Pull the image
+ subprocess.call(f"docker pull {tag}", shell=True)
+
+ # Run the Triton server and capture the container ID
+ container_id = (
+ subprocess.check_output(
+ f"docker run -d --rm -v {triton_repo}:/models -p 8000:8000 {tag} tritonserver --model-repository=/models",
+ shell=True,
+ )
+ .decode("utf-8")
+ .strip()
+ )
+
+ # Wait for the Triton server to start
+ triton_client = InferenceServerClient(url="localhost:8000", verbose=False, ssl=False)
+
+ # Wait until model is ready
+ for _ in range(10):
+ with contextlib.suppress(Exception):
+ assert triton_client.is_model_ready(model_name)
+ break
+ time.sleep(1)
+
+ # Check Triton inference
+ YOLO(f"http://localhost:8000/{model_name}", "detect")(SOURCE) # exported model inference
+
+ # Kill and remove the container at the end of the test
+ subprocess.call(f"docker kill {container_id}", shell=True)
+
+
+@pytest.mark.skipif(not check_requirements("pycocotools", install=False), reason="pycocotools not installed")
+def test_pycocotools():
+ """Validate YOLO model predictions on COCO dataset using pycocotools."""
+ from ultralytics.models.yolo.detect import DetectionValidator
+ from ultralytics.models.yolo.pose import PoseValidator
+ from ultralytics.models.yolo.segment import SegmentationValidator
+
+ # Download annotations after each dataset downloads first
+ url = "https://github.com/ultralytics/assets/releases/download/v0.0.0/"
+
+ args = {"model": "yolo11n.pt", "data": "coco8.yaml", "save_json": True, "imgsz": 64}
+ validator = DetectionValidator(args=args)
+ validator()
+ validator.is_coco = True
+ download(f"{url}instances_val2017.json", dir=DATASETS_DIR / "coco8/annotations")
+ _ = validator.eval_json(validator.stats)
+
+ args = {"model": "yolo11n-seg.pt", "data": "coco8-seg.yaml", "save_json": True, "imgsz": 64}
+ validator = SegmentationValidator(args=args)
+ validator()
+ validator.is_coco = True
+ download(f"{url}instances_val2017.json", dir=DATASETS_DIR / "coco8-seg/annotations")
+ _ = validator.eval_json(validator.stats)
+
+ args = {"model": "yolo11n-pose.pt", "data": "coco8-pose.yaml", "save_json": True, "imgsz": 64}
+ validator = PoseValidator(args=args)
+ validator()
+ validator.is_coco = True
+ download(f"{url}person_keypoints_val2017.json", dir=DATASETS_DIR / "coco8-pose/annotations")
+ _ = validator.eval_json(validator.stats)
diff --git a/tests/test_python.py b/tests/test_python.py
new file mode 100644
index 0000000000000000000000000000000000000000..644176fb4828668a83559e0c2ecdf915854ef85e
--- /dev/null
+++ b/tests/test_python.py
@@ -0,0 +1,615 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import contextlib
+import csv
+import urllib
+from copy import copy
+from pathlib import Path
+
+import cv2
+import numpy as np
+import pytest
+import torch
+import yaml
+from PIL import Image
+
+from tests import CFG, MODEL, SOURCE, SOURCES_LIST, TMP
+from ultralytics import RTDETR, YOLO
+from ultralytics.cfg import MODELS, TASK2DATA, TASKS
+from ultralytics.data.build import load_inference_source
+from ultralytics.utils import (
+ ASSETS,
+ DEFAULT_CFG,
+ DEFAULT_CFG_PATH,
+ LOGGER,
+ ONLINE,
+ ROOT,
+ WEIGHTS_DIR,
+ WINDOWS,
+ checks,
+ is_dir_writeable,
+ is_github_action_running,
+)
+from ultralytics.utils.downloads import download
+from ultralytics.utils.torch_utils import TORCH_1_9
+
+IS_TMP_WRITEABLE = is_dir_writeable(TMP) # WARNING: must be run once tests start as TMP does not exist on tests/init
+
+
+def test_model_forward():
+ """Test the forward pass of the YOLO model."""
+ model = YOLO(CFG)
+ model(source=None, imgsz=32, augment=True) # also test no source and augment
+
+
+def test_model_methods():
+ """Test various methods and properties of the YOLO model to ensure correct functionality."""
+ model = YOLO(MODEL)
+
+ # Model methods
+ model.info(verbose=True, detailed=True)
+ model = model.reset_weights()
+ model = model.load(MODEL)
+ model.to("cpu")
+ model.fuse()
+ model.clear_callback("on_train_start")
+ model.reset_callbacks()
+
+ # Model properties
+ _ = model.names
+ _ = model.device
+ _ = model.transforms
+ _ = model.task_map
+
+
+def test_model_profile():
+ """Test profiling of the YOLO model with `profile=True` to assess performance and resource usage."""
+ from ultralytics.nn.tasks import DetectionModel
+
+ model = DetectionModel() # build model
+ im = torch.randn(1, 3, 64, 64) # requires min imgsz=64
+ _ = model.predict(im, profile=True)
+
+
+@pytest.mark.skipif(not IS_TMP_WRITEABLE, reason="directory is not writeable")
+def test_predict_txt():
+ """Tests YOLO predictions with file, directory, and pattern sources listed in a text file."""
+ file = TMP / "sources_multi_row.txt"
+ with open(file, "w") as f:
+ for src in SOURCES_LIST:
+ f.write(f"{src}\n")
+ results = YOLO(MODEL)(source=file, imgsz=32)
+ assert len(results) == 7 # 1 + 2 + 2 + 2 = 7 images
+
+
+@pytest.mark.skipif(True, reason="disabled for testing")
+@pytest.mark.skipif(not IS_TMP_WRITEABLE, reason="directory is not writeable")
+def test_predict_csv_multi_row():
+ """Tests YOLO predictions with sources listed in multiple rows of a CSV file."""
+ file = TMP / "sources_multi_row.csv"
+ with open(file, "w", newline="") as f:
+ writer = csv.writer(f)
+ writer.writerow(["source"])
+ writer.writerows([[src] for src in SOURCES_LIST])
+ results = YOLO(MODEL)(source=file, imgsz=32)
+ assert len(results) == 7 # 1 + 2 + 2 + 2 = 7 images
+
+
+@pytest.mark.skipif(True, reason="disabled for testing")
+@pytest.mark.skipif(not IS_TMP_WRITEABLE, reason="directory is not writeable")
+def test_predict_csv_single_row():
+ """Tests YOLO predictions with sources listed in a single row of a CSV file."""
+ file = TMP / "sources_single_row.csv"
+ with open(file, "w", newline="") as f:
+ writer = csv.writer(f)
+ writer.writerow(SOURCES_LIST)
+ results = YOLO(MODEL)(source=file, imgsz=32)
+ assert len(results) == 7 # 1 + 2 + 2 + 2 = 7 images
+
+
+@pytest.mark.parametrize("model_name", MODELS)
+def test_predict_img(model_name):
+ """Test YOLO model predictions on various image input types and sources, including online images."""
+ model = YOLO(WEIGHTS_DIR / model_name)
+ im = cv2.imread(str(SOURCE)) # uint8 numpy array
+ assert len(model(source=Image.open(SOURCE), save=True, verbose=True, imgsz=32)) == 1 # PIL
+ assert len(model(source=im, save=True, save_txt=True, imgsz=32)) == 1 # ndarray
+ assert len(model(torch.rand((2, 3, 32, 32)), imgsz=32)) == 2 # batch-size 2 Tensor, FP32 0.0-1.0 RGB order
+ assert len(model(source=[im, im], save=True, save_txt=True, imgsz=32)) == 2 # batch
+ assert len(list(model(source=[im, im], save=True, stream=True, imgsz=32))) == 2 # stream
+ assert len(model(torch.zeros(320, 640, 3).numpy().astype(np.uint8), imgsz=32)) == 1 # tensor to numpy
+ batch = [
+ str(SOURCE), # filename
+ Path(SOURCE), # Path
+ "https://github.com/ultralytics/assets/releases/download/v0.0.0/zidane.jpg" if ONLINE else SOURCE, # URI
+ cv2.imread(str(SOURCE)), # OpenCV
+ Image.open(SOURCE), # PIL
+ np.zeros((320, 640, 3), dtype=np.uint8), # numpy
+ ]
+ assert len(model(batch, imgsz=32)) == len(batch) # multiple sources in a batch
+
+
+@pytest.mark.parametrize("model", MODELS)
+def test_predict_visualize(model):
+ """Test model prediction methods with 'visualize=True' to generate and display prediction visualizations."""
+ YOLO(WEIGHTS_DIR / model)(SOURCE, imgsz=32, visualize=True)
+
+
+def test_predict_grey_and_4ch():
+ """Test YOLO prediction on SOURCE converted to greyscale and 4-channel images with various filenames."""
+ im = Image.open(SOURCE)
+ directory = TMP / "im4"
+ directory.mkdir(parents=True, exist_ok=True)
+
+ source_greyscale = directory / "greyscale.jpg"
+ source_rgba = directory / "4ch.png"
+ source_non_utf = directory / "non_UTF_测试文件_tést_image.jpg"
+ source_spaces = directory / "image with spaces.jpg"
+
+ im.convert("L").save(source_greyscale) # greyscale
+ im.convert("RGBA").save(source_rgba) # 4-ch PNG with alpha
+ im.save(source_non_utf) # non-UTF characters in filename
+ im.save(source_spaces) # spaces in filename
+
+ # Inference
+ model = YOLO(MODEL)
+ for f in source_rgba, source_greyscale, source_non_utf, source_spaces:
+ for source in Image.open(f), cv2.imread(str(f)), f:
+ results = model(source, save=True, verbose=True, imgsz=32)
+ assert len(results) == 1 # verify that an image was run
+ f.unlink() # cleanup
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(not ONLINE, reason="environment is offline")
+@pytest.mark.skipif(is_github_action_running(), reason="No auth https://github.com/JuanBindez/pytubefix/issues/166")
+def test_youtube():
+ """Test YOLO model on a YouTube video stream, handling potential network-related errors."""
+ model = YOLO(MODEL)
+ try:
+ model.predict("https://youtu.be/G17sBkb38XQ", imgsz=96, save=True)
+ # Handle internet connection errors and 'urllib.error.HTTPError: HTTP Error 429: Too Many Requests'
+ except (urllib.error.HTTPError, ConnectionError) as e:
+ LOGGER.warning(f"WARNING: YouTube Test Error: {e}")
+
+
+@pytest.mark.skipif(not ONLINE, reason="environment is offline")
+@pytest.mark.skipif(not IS_TMP_WRITEABLE, reason="directory is not writeable")
+def test_track_stream():
+ """
+ Tests streaming tracking on a short 10 frame video using ByteTrack tracker and different GMC methods.
+
+ Note imgsz=160 required for tracking for higher confidence and better matches.
+ """
+ video_url = "https://github.com/ultralytics/assets/releases/download/v0.0.0/decelera_portrait_min.mov"
+ model = YOLO(MODEL)
+ model.track(video_url, imgsz=160, tracker="bytetrack.yaml")
+ model.track(video_url, imgsz=160, tracker="botsort.yaml", save_frames=True) # test frame saving also
+
+ # Test Global Motion Compensation (GMC) methods
+ for gmc in "orb", "sift", "ecc":
+ with open(ROOT / "cfg/trackers/botsort.yaml", encoding="utf-8") as f:
+ data = yaml.safe_load(f)
+ tracker = TMP / f"botsort-{gmc}.yaml"
+ data["gmc_method"] = gmc
+ with open(tracker, "w", encoding="utf-8") as f:
+ yaml.safe_dump(data, f)
+ model.track(video_url, imgsz=160, tracker=tracker)
+
+
+def test_val():
+ """Test the validation mode of the YOLO model."""
+ YOLO(MODEL).val(data="coco8.yaml", imgsz=32, save_hybrid=True)
+
+
+def test_train_scratch():
+ """Test training the YOLO model from scratch using the provided configuration."""
+ model = YOLO(CFG)
+ model.train(data="coco8.yaml", epochs=2, imgsz=32, cache="disk", batch=-1, close_mosaic=1, name="model")
+ model(SOURCE)
+
+
+def test_train_pretrained():
+ """Test training of the YOLO model starting from a pre-trained checkpoint."""
+ model = YOLO(WEIGHTS_DIR / "yolo11n-seg.pt")
+ model.train(data="coco8-seg.yaml", epochs=1, imgsz=32, cache="ram", copy_paste=0.5, mixup=0.5, name=0)
+ model(SOURCE)
+
+
+def test_all_model_yamls():
+ """Test YOLO model creation for all available YAML configurations in the `cfg/models` directory."""
+ for m in (ROOT / "cfg" / "models").rglob("*.yaml"):
+ if "rtdetr" in m.name:
+ if TORCH_1_9: # torch<=1.8 issue - TypeError: __init__() got an unexpected keyword argument 'batch_first'
+ _ = RTDETR(m.name)(SOURCE, imgsz=640) # must be 640
+ else:
+ YOLO(m.name)
+
+
+@pytest.mark.skipif(WINDOWS, reason="Windows slow CI export bug https://github.com/ultralytics/ultralytics/pull/16003")
+def test_workflow():
+ """Test the complete workflow including training, validation, prediction, and exporting."""
+ model = YOLO(MODEL)
+ model.train(data="coco8.yaml", epochs=1, imgsz=32, optimizer="SGD")
+ model.val(imgsz=32)
+ model.predict(SOURCE, imgsz=32)
+ model.export(format="torchscript") # WARNING: Windows slow CI export bug
+
+
+def test_predict_callback_and_setup():
+ """Test callback functionality during YOLO prediction setup and execution."""
+
+ def on_predict_batch_end(predictor):
+ """Callback function that handles operations at the end of a prediction batch."""
+ path, im0s, _ = predictor.batch
+ im0s = im0s if isinstance(im0s, list) else [im0s]
+ bs = [predictor.dataset.bs for _ in range(len(path))]
+ predictor.results = zip(predictor.results, im0s, bs) # results is List[batch_size]
+
+ model = YOLO(MODEL)
+ model.add_callback("on_predict_batch_end", on_predict_batch_end)
+
+ dataset = load_inference_source(source=SOURCE)
+ bs = dataset.bs # noqa access predictor properties
+ results = model.predict(dataset, stream=True, imgsz=160) # source already setup
+ for r, im0, bs in results:
+ print("test_callback", im0.shape)
+ print("test_callback", bs)
+ boxes = r.boxes # Boxes object for bbox outputs
+ print(boxes)
+
+
+@pytest.mark.parametrize("model", MODELS)
+def test_results(model):
+ """Ensure YOLO model predictions can be processed and printed in various formats."""
+ results = YOLO(WEIGHTS_DIR / model)([SOURCE, SOURCE], imgsz=160)
+ for r in results:
+ r = r.cpu().numpy()
+ print(r, len(r), r.path) # print numpy attributes
+ r = r.to(device="cpu", dtype=torch.float32)
+ r.save_txt(txt_file=TMP / "runs/tests/label.txt", save_conf=True)
+ r.save_crop(save_dir=TMP / "runs/tests/crops/")
+ r.to_json(normalize=True)
+ r.to_df(decimals=3)
+ r.to_csv()
+ r.to_xml()
+ r.plot(pil=True)
+ r.plot(conf=True, boxes=True)
+ print(r, len(r), r.path) # print after methods
+
+
+def test_labels_and_crops():
+ """Test output from prediction args for saving YOLO detection labels and crops; ensures accurate saving."""
+ imgs = [SOURCE, ASSETS / "zidane.jpg"]
+ results = YOLO(WEIGHTS_DIR / "yolo11n.pt")(imgs, imgsz=160, save_txt=True, save_crop=True)
+ save_path = Path(results[0].save_dir)
+ for r in results:
+ im_name = Path(r.path).stem
+ cls_idxs = r.boxes.cls.int().tolist()
+ # Check correct detections
+ assert cls_idxs == ([0, 7, 0, 0] if r.path.endswith("bus.jpg") else [0, 0, 0]) # bus.jpg and zidane.jpg classes
+ # Check label path
+ labels = save_path / f"labels/{im_name}.txt"
+ assert labels.exists()
+ # Check detections match label count
+ assert len(r.boxes.data) == len([line for line in labels.read_text().splitlines() if line])
+ # Check crops path and files
+ crop_dirs = list((save_path / "crops").iterdir())
+ crop_files = [f for p in crop_dirs for f in p.glob("*")]
+ # Crop directories match detections
+ assert all(r.names.get(c) in {d.name for d in crop_dirs} for c in cls_idxs)
+ # Same number of crops as detections
+ assert len([f for f in crop_files if im_name in f.name]) == len(r.boxes.data)
+
+
+@pytest.mark.skipif(not ONLINE, reason="environment is offline")
+def test_data_utils():
+ """Test utility functions in ultralytics/data/utils.py, including dataset stats and auto-splitting."""
+ from ultralytics.data.utils import HUBDatasetStats, autosplit
+ from ultralytics.utils.downloads import zip_directory
+
+ # from ultralytics.utils.files import WorkingDirectory
+ # with WorkingDirectory(ROOT.parent / 'tests'):
+
+ for task in TASKS:
+ file = Path(TASK2DATA[task]).with_suffix(".zip") # i.e. coco8.zip
+ download(f"https://github.com/ultralytics/hub/raw/main/example_datasets/{file}", unzip=False, dir=TMP)
+ stats = HUBDatasetStats(TMP / file, task=task)
+ stats.get_json(save=True)
+ stats.process_images()
+
+ autosplit(TMP / "coco8")
+ zip_directory(TMP / "coco8/images/val") # zip
+
+
+@pytest.mark.skipif(not ONLINE, reason="environment is offline")
+def test_data_converter():
+ """Test dataset conversion functions from COCO to YOLO format and class mappings."""
+ from ultralytics.data.converter import coco80_to_coco91_class, convert_coco
+
+ file = "instances_val2017.json"
+ download(f"https://github.com/ultralytics/assets/releases/download/v0.0.0/{file}", dir=TMP)
+ convert_coco(labels_dir=TMP, save_dir=TMP / "yolo_labels", use_segments=True, use_keypoints=False, cls91to80=True)
+ coco80_to_coco91_class()
+
+
+def test_data_annotator():
+ """Automatically annotate data using specified detection and segmentation models."""
+ from ultralytics.data.annotator import auto_annotate
+
+ auto_annotate(
+ ASSETS,
+ det_model=WEIGHTS_DIR / "yolo11n.pt",
+ sam_model=WEIGHTS_DIR / "mobile_sam.pt",
+ output_dir=TMP / "auto_annotate_labels",
+ )
+
+
+def test_events():
+ """Test event sending functionality."""
+ from ultralytics.hub.utils import Events
+
+ events = Events()
+ events.enabled = True
+ cfg = copy(DEFAULT_CFG) # does not require deepcopy
+ cfg.mode = "test"
+ events(cfg)
+
+
+def test_cfg_init():
+ """Test configuration initialization utilities from the 'ultralytics.cfg' module."""
+ from ultralytics.cfg import check_dict_alignment, copy_default_cfg, smart_value
+
+ with contextlib.suppress(SyntaxError):
+ check_dict_alignment({"a": 1}, {"b": 2})
+ copy_default_cfg()
+ (Path.cwd() / DEFAULT_CFG_PATH.name.replace(".yaml", "_copy.yaml")).unlink(missing_ok=False)
+ [smart_value(x) for x in ["none", "true", "false"]]
+
+
+def test_utils_init():
+ """Test initialization utilities in the Ultralytics library."""
+ from ultralytics.utils import get_git_branch, get_git_origin_url, get_ubuntu_version, is_github_action_running
+
+ get_ubuntu_version()
+ is_github_action_running()
+ get_git_origin_url()
+ get_git_branch()
+
+
+def test_utils_checks():
+ """Test various utility checks for filenames, git status, requirements, image sizes, and versions."""
+ checks.check_yolov5u_filename("yolov5n.pt")
+ checks.git_describe(ROOT)
+ checks.check_requirements() # check requirements.txt
+ checks.check_imgsz([600, 600], max_dim=1)
+ checks.check_imshow(warn=True)
+ checks.check_version("ultralytics", "8.0.0")
+ checks.print_args()
+
+
+@pytest.mark.skipif(WINDOWS, reason="Windows profiling is extremely slow (cause unknown)")
+def test_utils_benchmarks():
+ """Benchmark model performance using 'ProfileModels' from 'ultralytics.utils.benchmarks'."""
+ from ultralytics.utils.benchmarks import ProfileModels
+
+ ProfileModels(["yolo11n.yaml"], imgsz=32, min_time=1, num_timed_runs=3, num_warmup_runs=1).profile()
+
+
+def test_utils_torchutils():
+ """Test Torch utility functions including profiling and FLOP calculations."""
+ from ultralytics.nn.modules.conv import Conv
+ from ultralytics.utils.torch_utils import get_flops_with_torch_profiler, profile, time_sync
+
+ x = torch.randn(1, 64, 20, 20)
+ m = Conv(64, 64, k=1, s=2)
+
+ profile(x, [m], n=3)
+ get_flops_with_torch_profiler(m)
+ time_sync()
+
+
+def test_utils_ops():
+ """Test utility operations functions for coordinate transformation and normalization."""
+ from ultralytics.utils.ops import (
+ ltwh2xywh,
+ ltwh2xyxy,
+ make_divisible,
+ xywh2ltwh,
+ xywh2xyxy,
+ xywhn2xyxy,
+ xywhr2xyxyxyxy,
+ xyxy2ltwh,
+ xyxy2xywh,
+ xyxy2xywhn,
+ xyxyxyxy2xywhr,
+ )
+
+ make_divisible(17, torch.tensor([8]))
+
+ boxes = torch.rand(10, 4) # xywh
+ torch.allclose(boxes, xyxy2xywh(xywh2xyxy(boxes)))
+ torch.allclose(boxes, xyxy2xywhn(xywhn2xyxy(boxes)))
+ torch.allclose(boxes, ltwh2xywh(xywh2ltwh(boxes)))
+ torch.allclose(boxes, xyxy2ltwh(ltwh2xyxy(boxes)))
+
+ boxes = torch.rand(10, 5) # xywhr for OBB
+ boxes[:, 4] = torch.randn(10) * 30
+ torch.allclose(boxes, xyxyxyxy2xywhr(xywhr2xyxyxyxy(boxes)), rtol=1e-3)
+
+
+def test_utils_files():
+ """Test file handling utilities including file age, date, and paths with spaces."""
+ from ultralytics.utils.files import file_age, file_date, get_latest_run, spaces_in_path
+
+ file_age(SOURCE)
+ file_date(SOURCE)
+ get_latest_run(ROOT / "runs")
+
+ path = TMP / "path/with spaces"
+ path.mkdir(parents=True, exist_ok=True)
+ with spaces_in_path(path) as new_path:
+ print(new_path)
+
+
+@pytest.mark.slow
+def test_utils_patches_torch_save():
+ """Test torch_save backoff when _torch_save raises RuntimeError to ensure robustness."""
+ from unittest.mock import MagicMock, patch
+
+ from ultralytics.utils.patches import torch_save
+
+ mock = MagicMock(side_effect=RuntimeError)
+
+ with patch("ultralytics.utils.patches._torch_save", new=mock):
+ with pytest.raises(RuntimeError):
+ torch_save(torch.zeros(1), TMP / "test.pt")
+
+ assert mock.call_count == 4, "torch_save was not attempted the expected number of times"
+
+
+def test_nn_modules_conv():
+ """Test Convolutional Neural Network modules including CBAM, Conv2, and ConvTranspose."""
+ from ultralytics.nn.modules.conv import CBAM, Conv2, ConvTranspose, DWConvTranspose2d, Focus
+
+ c1, c2 = 8, 16 # input and output channels
+ x = torch.zeros(4, c1, 10, 10) # BCHW
+
+ # Run all modules not otherwise covered in tests
+ DWConvTranspose2d(c1, c2)(x)
+ ConvTranspose(c1, c2)(x)
+ Focus(c1, c2)(x)
+ CBAM(c1)(x)
+
+ # Fuse ops
+ m = Conv2(c1, c2)
+ m.fuse_convs()
+ m(x)
+
+
+def test_nn_modules_block():
+ """Test various blocks in neural network modules including C1, C3TR, BottleneckCSP, C3Ghost, and C3x."""
+ from ultralytics.nn.modules.block import C1, C3TR, BottleneckCSP, C3Ghost, C3x
+
+ c1, c2 = 8, 16 # input and output channels
+ x = torch.zeros(4, c1, 10, 10) # BCHW
+
+ # Run all modules not otherwise covered in tests
+ C1(c1, c2)(x)
+ C3x(c1, c2)(x)
+ C3TR(c1, c2)(x)
+ C3Ghost(c1, c2)(x)
+ BottleneckCSP(c1, c2)(x)
+
+
+@pytest.mark.skipif(not ONLINE, reason="environment is offline")
+def test_hub():
+ """Test Ultralytics HUB functionalities (e.g. export formats, logout)."""
+ from ultralytics.hub import export_fmts_hub, logout
+ from ultralytics.hub.utils import smart_request
+
+ export_fmts_hub()
+ logout()
+ smart_request("GET", "https://github.com", progress=True)
+
+
+@pytest.fixture
+def image():
+ """Load and return an image from a predefined source using OpenCV."""
+ return cv2.imread(str(SOURCE))
+
+
+@pytest.mark.parametrize(
+ "auto_augment, erasing, force_color_jitter",
+ [
+ (None, 0.0, False),
+ ("randaugment", 0.5, True),
+ ("augmix", 0.2, False),
+ ("autoaugment", 0.0, True),
+ ],
+)
+def test_classify_transforms_train(image, auto_augment, erasing, force_color_jitter):
+ """Tests classification transforms during training with various augmentations to ensure proper functionality."""
+ from ultralytics.data.augment import classify_augmentations
+
+ transform = classify_augmentations(
+ size=224,
+ mean=(0.5, 0.5, 0.5),
+ std=(0.5, 0.5, 0.5),
+ scale=(0.08, 1.0),
+ ratio=(3.0 / 4.0, 4.0 / 3.0),
+ hflip=0.5,
+ vflip=0.5,
+ auto_augment=auto_augment,
+ hsv_h=0.015,
+ hsv_s=0.4,
+ hsv_v=0.4,
+ force_color_jitter=force_color_jitter,
+ erasing=erasing,
+ )
+
+ transformed_image = transform(Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)))
+
+ assert transformed_image.shape == (3, 224, 224)
+ assert torch.is_tensor(transformed_image)
+ assert transformed_image.dtype == torch.float32
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(not ONLINE, reason="environment is offline")
+def test_model_tune():
+ """Tune YOLO model for performance improvement."""
+ YOLO("yolo11n-pose.pt").tune(data="coco8-pose.yaml", plots=False, imgsz=32, epochs=1, iterations=2, device="cpu")
+ YOLO("yolo11n-cls.pt").tune(data="imagenet10", plots=False, imgsz=32, epochs=1, iterations=2, device="cpu")
+
+
+def test_model_embeddings():
+ """Test YOLO model embeddings."""
+ model_detect = YOLO(MODEL)
+ model_segment = YOLO(WEIGHTS_DIR / "yolo11n-seg.pt")
+
+ for batch in [SOURCE], [SOURCE, SOURCE]: # test batch size 1 and 2
+ assert len(model_detect.embed(source=batch, imgsz=32)) == len(batch)
+ assert len(model_segment.embed(source=batch, imgsz=32)) == len(batch)
+
+
+@pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="YOLOWorld with CLIP is not supported in Python 3.12")
+def test_yolo_world():
+ """Tests YOLO world models with CLIP support, including detection and training scenarios."""
+ model = YOLO(WEIGHTS_DIR / "yolov8s-world.pt") # no YOLO11n-world model yet
+ model.set_classes(["tree", "window"])
+ model(SOURCE, conf=0.01)
+
+ model = YOLO(WEIGHTS_DIR / "yolov8s-worldv2.pt") # no YOLO11n-world model yet
+ # Training from a pretrained model. Eval is included at the final stage of training.
+ # Use dota8.yaml which has fewer categories to reduce the inference time of CLIP model
+ model.train(
+ data="dota8.yaml",
+ epochs=1,
+ imgsz=32,
+ cache="disk",
+ close_mosaic=1,
+ )
+
+ # test WorWorldTrainerFromScratch
+ from ultralytics.models.yolo.world.train_world import WorldTrainerFromScratch
+
+ model = YOLO("yolov8s-worldv2.yaml") # no YOLO11n-world model yet
+ model.train(
+ data={"train": {"yolo_data": ["dota8.yaml"]}, "val": {"yolo_data": ["dota8.yaml"]}},
+ epochs=1,
+ imgsz=32,
+ cache="disk",
+ close_mosaic=1,
+ trainer=WorldTrainerFromScratch,
+ )
+
+
+def test_yolov10():
+ """Test YOLOv10 model training, validation, and prediction steps with minimal configurations."""
+ model = YOLO("yolov10n.yaml")
+ # train/val/predict
+ model.train(data="coco8.yaml", epochs=1, imgsz=32, close_mosaic=1, cache="disk")
+ model.val(data="coco8.yaml", imgsz=32)
+ model.predict(imgsz=32, save_txt=True, save_crop=True, augment=True)
+ model(SOURCE)
diff --git a/tests/test_solutions.py b/tests/test_solutions.py
new file mode 100644
index 0000000000000000000000000000000000000000..056a056fbc1e0321b4ce506ac146e2a2108aec7e
--- /dev/null
+++ b/tests/test_solutions.py
@@ -0,0 +1,94 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import cv2
+import pytest
+
+from tests import TMP
+from ultralytics import YOLO, solutions
+from ultralytics.utils import ASSETS_URL, WEIGHTS_DIR
+from ultralytics.utils.downloads import safe_download
+
+DEMO_VIDEO = "solutions_ci_demo.mp4"
+POSE_VIDEO = "solution_ci_pose_demo.mp4"
+
+
+@pytest.mark.slow
+def test_major_solutions():
+ """Test the object counting, heatmap, speed estimation, trackzone and queue management solution."""
+ safe_download(url=f"{ASSETS_URL}/{DEMO_VIDEO}", dir=TMP)
+ cap = cv2.VideoCapture(str(TMP / DEMO_VIDEO))
+ assert cap.isOpened(), "Error reading video file"
+ region_points = [(20, 400), (1080, 400), (1080, 360), (20, 360)]
+ counter = solutions.ObjectCounter(region=region_points, model="yolo11n.pt", show=False) # Test object counter
+ heatmap = solutions.Heatmap(colormap=cv2.COLORMAP_PARULA, model="yolo11n.pt", show=False) # Test heatmaps
+ heatmap_count = solutions.Heatmap(
+ colormap=cv2.COLORMAP_PARULA, model="yolo11n.pt", show=False, region=region_points
+ ) # Test heatmaps with object counting
+ speed = solutions.SpeedEstimator(region=region_points, model="yolo11n.pt", show=False) # Test queue manager
+ queue = solutions.QueueManager(region=region_points, model="yolo11n.pt", show=False) # Test speed estimation
+ line_analytics = solutions.Analytics(analytics_type="line", model="yolo11n.pt", show=False) # line analytics
+ pie_analytics = solutions.Analytics(analytics_type="pie", model="yolo11n.pt", show=False) # line analytics
+ bar_analytics = solutions.Analytics(analytics_type="bar", model="yolo11n.pt", show=False) # line analytics
+ area_analytics = solutions.Analytics(analytics_type="area", model="yolo11n.pt", show=False) # line analytics
+ trackzone = solutions.TrackZone(region=region_points, model="yolo11n.pt", show=False) # Test trackzone
+ frame_count = 0 # Required for analytics
+ while cap.isOpened():
+ success, im0 = cap.read()
+ if not success:
+ break
+ frame_count += 1
+ original_im0 = im0.copy()
+ _ = counter.count(original_im0.copy())
+ _ = heatmap.generate_heatmap(original_im0.copy())
+ _ = heatmap_count.generate_heatmap(original_im0.copy())
+ _ = speed.estimate_speed(original_im0.copy())
+ _ = queue.process_queue(original_im0.copy())
+ _ = line_analytics.process_data(original_im0.copy(), frame_count)
+ _ = pie_analytics.process_data(original_im0.copy(), frame_count)
+ _ = bar_analytics.process_data(original_im0.copy(), frame_count)
+ _ = area_analytics.process_data(original_im0.copy(), frame_count)
+ _ = trackzone.trackzone(original_im0.copy())
+ cap.release()
+
+ # Test workouts monitoring
+ safe_download(url=f"{ASSETS_URL}/{POSE_VIDEO}", dir=TMP)
+ cap = cv2.VideoCapture(str(TMP / POSE_VIDEO))
+ assert cap.isOpened(), "Error reading video file"
+ gym = solutions.AIGym(kpts=[5, 11, 13], show=False)
+ while cap.isOpened():
+ success, im0 = cap.read()
+ if not success:
+ break
+ _ = gym.monitor(im0)
+ cap.release()
+
+
+@pytest.mark.slow
+def test_instance_segmentation():
+ """Test the instance segmentation solution."""
+ from ultralytics.utils.plotting import Annotator, colors
+
+ model = YOLO(WEIGHTS_DIR / "yolo11n-seg.pt")
+ names = model.names
+ cap = cv2.VideoCapture(TMP / DEMO_VIDEO)
+ assert cap.isOpened(), "Error reading video file"
+ while cap.isOpened():
+ success, im0 = cap.read()
+ if not success:
+ break
+ results = model.predict(im0)
+ annotator = Annotator(im0, line_width=2)
+ if results[0].masks is not None:
+ clss = results[0].boxes.cls.cpu().tolist()
+ masks = results[0].masks.xy
+ for mask, cls in zip(masks, clss):
+ color = colors(int(cls), True)
+ annotator.seg_bbox(mask=mask, mask_color=color, label=names[int(cls)])
+ cap.release()
+ cv2.destroyAllWindows()
+
+
+@pytest.mark.slow
+def test_streamlit_predict():
+ """Test streamlit predict live inference solution."""
+ solutions.Inference().inference()
diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..d15296f1584c3773b59a050e31e3e32a0acea102
--- /dev/null
+++ b/ultralytics/__init__.py
@@ -0,0 +1,29 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+__version__ = "8.3.63"
+
+import os
+
+# Set ENV variables (place before imports)
+if not os.environ.get("OMP_NUM_THREADS"):
+ os.environ["OMP_NUM_THREADS"] = "1" # default for reduced CPU utilization during training
+
+from ultralytics.models import NAS, RTDETR, SAM, YOLO, FastSAM, YOLOWorld
+from ultralytics.utils import ASSETS, SETTINGS
+from ultralytics.utils.checks import check_yolo as checks
+from ultralytics.utils.downloads import download
+
+settings = SETTINGS
+__all__ = (
+ "__version__",
+ "ASSETS",
+ "YOLO",
+ "YOLOWorld",
+ "NAS",
+ "SAM",
+ "FastSAM",
+ "RTDETR",
+ "checks",
+ "download",
+ "settings",
+)
diff --git a/ultralytics/assets/bus.jpg b/ultralytics/assets/bus.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..41bda57f9b2b3fb2c5ba1163fd738e151f2b81d7
--- /dev/null
+++ b/ultralytics/assets/bus.jpg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c02019c4979c191eb739ddd944445ef408dad5679acab6fd520ef9d434bfbc63
+size 137419
diff --git a/ultralytics/assets/zidane.jpg b/ultralytics/assets/zidane.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..eeab1cdcb282b0e026a57c5bf85df36024b4e1f6
Binary files /dev/null and b/ultralytics/assets/zidane.jpg differ
diff --git a/ultralytics/cfg/__init__.py b/ultralytics/cfg/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8625f7c99cf4995822c53e14930de5f9a59db3a2
--- /dev/null
+++ b/ultralytics/cfg/__init__.py
@@ -0,0 +1,1025 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import shutil
+import subprocess
+import sys
+from pathlib import Path
+from types import SimpleNamespace
+from typing import Dict, List, Union
+
+import cv2
+
+from ultralytics.utils import (
+ ASSETS,
+ DEFAULT_CFG,
+ DEFAULT_CFG_DICT,
+ DEFAULT_CFG_PATH,
+ DEFAULT_SOL_DICT,
+ IS_VSCODE,
+ LOGGER,
+ RANK,
+ ROOT,
+ RUNS_DIR,
+ SETTINGS,
+ SETTINGS_FILE,
+ TESTS_RUNNING,
+ IterableSimpleNamespace,
+ __version__,
+ checks,
+ colorstr,
+ deprecation_warn,
+ vscode_msg,
+ yaml_load,
+ yaml_print,
+)
+
+# Define valid solutions
+SOLUTION_MAP = {
+ "count": ("ObjectCounter", "count"),
+ "heatmap": ("Heatmap", "generate_heatmap"),
+ "queue": ("QueueManager", "process_queue"),
+ "speed": ("SpeedEstimator", "estimate_speed"),
+ "workout": ("AIGym", "monitor"),
+ "analytics": ("Analytics", "process_data"),
+ "trackzone": ("TrackZone", "trackzone"),
+ "inference": ("Inference", "inference"),
+ "help": None,
+}
+
+# Define valid tasks and modes
+MODES = {"train", "val", "predict", "export", "track", "benchmark"}
+TASKS = {"detect", "segment", "classify", "pose", "obb"}
+TASK2DATA = {
+ "detect": "coco8.yaml",
+ "segment": "coco8-seg.yaml",
+ "classify": "imagenet10",
+ "pose": "coco8-pose.yaml",
+ "obb": "dota8.yaml",
+}
+TASK2MODEL = {
+ "detect": "yolo11n.pt",
+ "segment": "yolo11n-seg.pt",
+ "classify": "yolo11n-cls.pt",
+ "pose": "yolo11n-pose.pt",
+ "obb": "yolo11n-obb.pt",
+}
+TASK2METRIC = {
+ "detect": "metrics/mAP50-95(B)",
+ "segment": "metrics/mAP50-95(M)",
+ "classify": "metrics/accuracy_top1",
+ "pose": "metrics/mAP50-95(P)",
+ "obb": "metrics/mAP50-95(B)",
+}
+MODELS = {TASK2MODEL[task] for task in TASKS}
+
+ARGV = sys.argv or ["", ""] # sometimes sys.argv = []
+SOLUTIONS_HELP_MSG = f"""
+ Arguments received: {str(["yolo"] + ARGV[1:])}. Ultralytics 'yolo solutions' usage overview:
+
+ yolo solutions SOLUTION ARGS
+
+ Where SOLUTION (optional) is one of {list(SOLUTION_MAP.keys())[:-1]}
+ ARGS (optional) are any number of custom 'arg=value' pairs like 'show_in=True' that override defaults
+ at https://docs.ultralytics.com/usage/cfg
+
+ 1. Call object counting solution
+ yolo solutions count source="path/to/video/file.mp4" region=[(20, 400), (1080, 400), (1080, 360), (20, 360)]
+
+ 2. Call heatmaps solution
+ yolo solutions heatmap colormap=cv2.COLORMAP_PARULA model=yolo11n.pt
+
+ 3. Call queue management solution
+ yolo solutions queue region=[(20, 400), (1080, 400), (1080, 360), (20, 360)] model=yolo11n.pt
+
+ 4. Call workouts monitoring solution for push-ups
+ yolo solutions workout model=yolo11n-pose.pt kpts=[6, 8, 10]
+
+ 5. Generate analytical graphs
+ yolo solutions analytics analytics_type="pie"
+
+ 6. Track objects within specific zones
+ yolo solutions trackzone source="path/to/video/file.mp4" region=[(150, 150), (1130, 150), (1130, 570), (150, 570)]
+
+ 7. Streamlit real-time webcam inference GUI
+ yolo streamlit-predict
+ """
+CLI_HELP_MSG = f"""
+ Arguments received: {str(["yolo"] + ARGV[1:])}. Ultralytics 'yolo' commands use the following syntax:
+
+ yolo TASK MODE ARGS
+
+ Where TASK (optional) is one of {TASKS}
+ MODE (required) is one of {MODES}
+ ARGS (optional) are any number of custom 'arg=value' pairs like 'imgsz=320' that override defaults.
+ See all ARGS at https://docs.ultralytics.com/usage/cfg or with 'yolo cfg'
+
+ 1. Train a detection model for 10 epochs with an initial learning_rate of 0.01
+ yolo train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01
+
+ 2. Predict a YouTube video using a pretrained segmentation model at image size 320:
+ yolo predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
+
+ 3. Val a pretrained detection model at batch-size 1 and image size 640:
+ yolo val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640
+
+ 4. Export a YOLO11n classification model to ONNX format at image size 224 by 128 (no TASK required)
+ yolo export model=yolo11n-cls.pt format=onnx imgsz=224,128
+
+ 5. Ultralytics solutions usage
+ yolo solutions count or in {list(SOLUTION_MAP.keys())[1:-1]} source="path/to/video/file.mp4"
+
+ 6. Run special commands:
+ yolo help
+ yolo checks
+ yolo version
+ yolo settings
+ yolo copy-cfg
+ yolo cfg
+ yolo solutions help
+
+ Docs: https://docs.ultralytics.com
+ Solutions: https://docs.ultralytics.com/solutions/
+ Community: https://community.ultralytics.com
+ GitHub: https://github.com/ultralytics/ultralytics
+ """
+
+# Define keys for arg type checks
+CFG_FLOAT_KEYS = { # integer or float arguments, i.e. x=2 and x=2.0
+ "warmup_epochs",
+ "box",
+ "cls",
+ "dfl",
+ "degrees",
+ "shear",
+ "time",
+ "workspace",
+ "batch",
+}
+CFG_FRACTION_KEYS = { # fractional float arguments with 0.0<=values<=1.0
+ "dropout",
+ "lr0",
+ "lrf",
+ "momentum",
+ "weight_decay",
+ "warmup_momentum",
+ "warmup_bias_lr",
+ "hsv_h",
+ "hsv_s",
+ "hsv_v",
+ "translate",
+ "scale",
+ "perspective",
+ "flipud",
+ "fliplr",
+ "bgr",
+ "mosaic",
+ "mixup",
+ "copy_paste",
+ "conf",
+ "iou",
+ "fraction",
+}
+CFG_INT_KEYS = { # integer-only arguments
+ "epochs",
+ "patience",
+ "workers",
+ "seed",
+ "close_mosaic",
+ "mask_ratio",
+ "max_det",
+ "vid_stride",
+ "line_width",
+ "nbs",
+ "save_period",
+}
+CFG_BOOL_KEYS = { # boolean-only arguments
+ "save",
+ "exist_ok",
+ "verbose",
+ "deterministic",
+ "single_cls",
+ "rect",
+ "cos_lr",
+ "overlap_mask",
+ "val",
+ "save_json",
+ "save_hybrid",
+ "half",
+ "dnn",
+ "plots",
+ "show",
+ "save_txt",
+ "save_conf",
+ "save_crop",
+ "save_frames",
+ "show_labels",
+ "show_conf",
+ "visualize",
+ "augment",
+ "agnostic_nms",
+ "retina_masks",
+ "show_boxes",
+ "keras",
+ "optimize",
+ "int8",
+ "dynamic",
+ "simplify",
+ "nms",
+ "profile",
+ "multi_scale",
+}
+
+
+def cfg2dict(cfg):
+ """
+ Converts a configuration object to a dictionary.
+
+ Args:
+ cfg (str | Path | Dict | SimpleNamespace): Configuration object to be converted. Can be a file path,
+ a string, a dictionary, or a SimpleNamespace object.
+
+ Returns:
+ (Dict): Configuration object in dictionary format.
+
+ Examples:
+ Convert a YAML file path to a dictionary:
+ >>> config_dict = cfg2dict("config.yaml")
+
+ Convert a SimpleNamespace to a dictionary:
+ >>> from types import SimpleNamespace
+ >>> config_sn = SimpleNamespace(param1="value1", param2="value2")
+ >>> config_dict = cfg2dict(config_sn)
+
+ Pass through an already existing dictionary:
+ >>> config_dict = cfg2dict({"param1": "value1", "param2": "value2"})
+
+ Notes:
+ - If cfg is a path or string, it's loaded as YAML and converted to a dictionary.
+ - If cfg is a SimpleNamespace object, it's converted to a dictionary using vars().
+ - If cfg is already a dictionary, it's returned unchanged.
+ """
+ if isinstance(cfg, (str, Path)):
+ cfg = yaml_load(cfg) # load dict
+ elif isinstance(cfg, SimpleNamespace):
+ cfg = vars(cfg) # convert to dict
+ return cfg
+
+
+def get_cfg(cfg: Union[str, Path, Dict, SimpleNamespace] = DEFAULT_CFG_DICT, overrides: Dict = None):
+ """
+ Load and merge configuration data from a file or dictionary, with optional overrides.
+
+ Args:
+ cfg (str | Path | Dict | SimpleNamespace): Configuration data source. Can be a file path, dictionary, or
+ SimpleNamespace object.
+ overrides (Dict | None): Dictionary containing key-value pairs to override the base configuration.
+
+ Returns:
+ (SimpleNamespace): Namespace containing the merged configuration arguments.
+
+ Examples:
+ >>> from ultralytics.cfg import get_cfg
+ >>> config = get_cfg() # Load default configuration
+ >>> config_with_overrides = get_cfg("path/to/config.yaml", overrides={"epochs": 50, "batch_size": 16})
+
+ Notes:
+ - If both `cfg` and `overrides` are provided, the values in `overrides` will take precedence.
+ - Special handling ensures alignment and correctness of the configuration, such as converting numeric
+ `project` and `name` to strings and validating configuration keys and values.
+ - The function performs type and value checks on the configuration data.
+ """
+ cfg = cfg2dict(cfg)
+
+ # Merge overrides
+ if overrides:
+ overrides = cfg2dict(overrides)
+ if "save_dir" not in cfg:
+ overrides.pop("save_dir", None) # special override keys to ignore
+ check_dict_alignment(cfg, overrides)
+ cfg = {**cfg, **overrides} # merge cfg and overrides dicts (prefer overrides)
+
+ # Special handling for numeric project/name
+ for k in "project", "name":
+ if k in cfg and isinstance(cfg[k], (int, float)):
+ cfg[k] = str(cfg[k])
+ if cfg.get("name") == "model": # assign model to 'name' arg
+ cfg["name"] = str(cfg.get("model", "")).split(".")[0]
+ LOGGER.warning(f"WARNING ⚠️ 'name=model' automatically updated to 'name={cfg['name']}'.")
+
+ # Type and Value checks
+ check_cfg(cfg)
+
+ # Return instance
+ return IterableSimpleNamespace(**cfg)
+
+
+def check_cfg(cfg, hard=True):
+ """
+ Checks configuration argument types and values for the Ultralytics library.
+
+ This function validates the types and values of configuration arguments, ensuring correctness and converting
+ them if necessary. It checks for specific key types defined in global variables such as CFG_FLOAT_KEYS,
+ CFG_FRACTION_KEYS, CFG_INT_KEYS, and CFG_BOOL_KEYS.
+
+ Args:
+ cfg (Dict): Configuration dictionary to validate.
+ hard (bool): If True, raises exceptions for invalid types and values; if False, attempts to convert them.
+
+ Examples:
+ >>> config = {
+ ... "epochs": 50, # valid integer
+ ... "lr0": 0.01, # valid float
+ ... "momentum": 1.2, # invalid float (out of 0.0-1.0 range)
+ ... "save": "true", # invalid bool
+ ... }
+ >>> check_cfg(config, hard=False)
+ >>> print(config)
+ {'epochs': 50, 'lr0': 0.01, 'momentum': 1.2, 'save': False} # corrected 'save' key
+
+ Notes:
+ - The function modifies the input dictionary in-place.
+ - None values are ignored as they may be from optional arguments.
+ - Fraction keys are checked to be within the range [0.0, 1.0].
+ """
+ for k, v in cfg.items():
+ if v is not None: # None values may be from optional args
+ if k in CFG_FLOAT_KEYS and not isinstance(v, (int, float)):
+ if hard:
+ raise TypeError(
+ f"'{k}={v}' is of invalid type {type(v).__name__}. "
+ f"Valid '{k}' types are int (i.e. '{k}=0') or float (i.e. '{k}=0.5')"
+ )
+ cfg[k] = float(v)
+ elif k in CFG_FRACTION_KEYS:
+ if not isinstance(v, (int, float)):
+ if hard:
+ raise TypeError(
+ f"'{k}={v}' is of invalid type {type(v).__name__}. "
+ f"Valid '{k}' types are int (i.e. '{k}=0') or float (i.e. '{k}=0.5')"
+ )
+ cfg[k] = v = float(v)
+ if not (0.0 <= v <= 1.0):
+ raise ValueError(f"'{k}={v}' is an invalid value. Valid '{k}' values are between 0.0 and 1.0.")
+ elif k in CFG_INT_KEYS and not isinstance(v, int):
+ if hard:
+ raise TypeError(
+ f"'{k}={v}' is of invalid type {type(v).__name__}. '{k}' must be an int (i.e. '{k}=8')"
+ )
+ cfg[k] = int(v)
+ elif k in CFG_BOOL_KEYS and not isinstance(v, bool):
+ if hard:
+ raise TypeError(
+ f"'{k}={v}' is of invalid type {type(v).__name__}. "
+ f"'{k}' must be a bool (i.e. '{k}=True' or '{k}=False')"
+ )
+ cfg[k] = bool(v)
+
+
+def get_save_dir(args, name=None):
+ """
+ Returns the directory path for saving outputs, derived from arguments or default settings.
+
+ Args:
+ args (SimpleNamespace): Namespace object containing configurations such as 'project', 'name', 'task',
+ 'mode', and 'save_dir'.
+ name (str | None): Optional name for the output directory. If not provided, it defaults to 'args.name'
+ or the 'args.mode'.
+
+ Returns:
+ (Path): Directory path where outputs should be saved.
+
+ Examples:
+ >>> from types import SimpleNamespace
+ >>> args = SimpleNamespace(project="my_project", task="detect", mode="train", exist_ok=True)
+ >>> save_dir = get_save_dir(args)
+ >>> print(save_dir)
+ my_project/detect/train
+ """
+ if getattr(args, "save_dir", None):
+ save_dir = args.save_dir
+ else:
+ from ultralytics.utils.files import increment_path
+
+ project = args.project or (ROOT.parent / "tests/tmp/runs" if TESTS_RUNNING else RUNS_DIR) / args.task
+ name = name or args.name or f"{args.mode}"
+ save_dir = increment_path(Path(project) / name, exist_ok=args.exist_ok if RANK in {-1, 0} else True)
+
+ return Path(save_dir)
+
+
+def _handle_deprecation(custom):
+ """
+ Handles deprecated configuration keys by mapping them to current equivalents with deprecation warnings.
+
+ Args:
+ custom (Dict): Configuration dictionary potentially containing deprecated keys.
+
+ Examples:
+ >>> custom_config = {"boxes": True, "hide_labels": "False", "line_thickness": 2}
+ >>> _handle_deprecation(custom_config)
+ >>> print(custom_config)
+ {'show_boxes': True, 'show_labels': True, 'line_width': 2}
+
+ Notes:
+ This function modifies the input dictionary in-place, replacing deprecated keys with their current
+ equivalents. It also handles value conversions where necessary, such as inverting boolean values for
+ 'hide_labels' and 'hide_conf'.
+ """
+ for key in custom.copy().keys():
+ if key == "boxes":
+ deprecation_warn(key, "show_boxes")
+ custom["show_boxes"] = custom.pop("boxes")
+ if key == "hide_labels":
+ deprecation_warn(key, "show_labels")
+ custom["show_labels"] = custom.pop("hide_labels") == "False"
+ if key == "hide_conf":
+ deprecation_warn(key, "show_conf")
+ custom["show_conf"] = custom.pop("hide_conf") == "False"
+ if key == "line_thickness":
+ deprecation_warn(key, "line_width")
+ custom["line_width"] = custom.pop("line_thickness")
+ if key == "label_smoothing":
+ deprecation_warn(key)
+ custom.pop("label_smoothing")
+
+ return custom
+
+
+def check_dict_alignment(base: Dict, custom: Dict, e=None):
+ """
+ Checks alignment between custom and base configuration dictionaries, handling deprecated keys and providing error
+ messages for mismatched keys.
+
+ Args:
+ base (Dict): The base configuration dictionary containing valid keys.
+ custom (Dict): The custom configuration dictionary to be checked for alignment.
+ e (Exception | None): Optional error instance passed by the calling function.
+
+ Raises:
+ SystemExit: If mismatched keys are found between the custom and base dictionaries.
+
+ Examples:
+ >>> base_cfg = {"epochs": 50, "lr0": 0.01, "batch_size": 16}
+ >>> custom_cfg = {"epoch": 100, "lr": 0.02, "batch_size": 32}
+ >>> try:
+ ... check_dict_alignment(base_cfg, custom_cfg)
+ ... except SystemExit:
+ ... print("Mismatched keys found")
+
+ Notes:
+ - Suggests corrections for mismatched keys based on similarity to valid keys.
+ - Automatically replaces deprecated keys in the custom configuration with updated equivalents.
+ - Prints detailed error messages for each mismatched key to help users correct their configurations.
+ """
+ custom = _handle_deprecation(custom)
+ base_keys, custom_keys = (set(x.keys()) for x in (base, custom))
+ if mismatched := [k for k in custom_keys if k not in base_keys]:
+ from difflib import get_close_matches
+
+ string = ""
+ for x in mismatched:
+ matches = get_close_matches(x, base_keys) # key list
+ matches = [f"{k}={base[k]}" if base.get(k) is not None else k for k in matches]
+ match_str = f"Similar arguments are i.e. {matches}." if matches else ""
+ string += f"'{colorstr('red', 'bold', x)}' is not a valid YOLO argument. {match_str}\n"
+ raise SyntaxError(string + CLI_HELP_MSG) from e
+
+
+def merge_equals_args(args: List[str]) -> List[str]:
+ """
+ Merges arguments around isolated '=' in a list of strings and joins fragments with brackets.
+
+ This function handles the following cases:
+ 1. ['arg', '=', 'val'] becomes ['arg=val']
+ 2. ['arg=', 'val'] becomes ['arg=val']
+ 3. ['arg', '=val'] becomes ['arg=val']
+ 4. Joins fragments with brackets, e.g., ['imgsz=[3,', '640,', '640]'] becomes ['imgsz=[3,640,640]']
+
+ Args:
+ args (List[str]): A list of strings where each element represents an argument or fragment.
+
+ Returns:
+ List[str]: A list of strings where the arguments around isolated '=' are merged and fragments with brackets are joined.
+
+ Examples:
+ >>> args = ["arg1", "=", "value", "arg2=", "value2", "arg3", "=value3", "imgsz=[3,", "640,", "640]"]
+ >>> merge_and_join_args(args)
+ ['arg1=value', 'arg2=value2', 'arg3=value3', 'imgsz=[3,640,640]']
+ """
+ new_args = []
+ current = ""
+ depth = 0
+
+ i = 0
+ while i < len(args):
+ arg = args[i]
+
+ # Handle equals sign merging
+ if arg == "=" and 0 < i < len(args) - 1: # merge ['arg', '=', 'val']
+ new_args[-1] += f"={args[i + 1]}"
+ i += 2
+ continue
+ elif arg.endswith("=") and i < len(args) - 1 and "=" not in args[i + 1]: # merge ['arg=', 'val']
+ new_args.append(f"{arg}{args[i + 1]}")
+ i += 2
+ continue
+ elif arg.startswith("=") and i > 0: # merge ['arg', '=val']
+ new_args[-1] += arg
+ i += 1
+ continue
+
+ # Handle bracket joining
+ depth += arg.count("[") - arg.count("]")
+ current += arg
+ if depth == 0:
+ new_args.append(current)
+ current = ""
+
+ i += 1
+
+ # Append any remaining current string
+ if current:
+ new_args.append(current)
+
+ return new_args
+
+
+def handle_yolo_hub(args: List[str]) -> None:
+ """
+ Handles Ultralytics HUB command-line interface (CLI) commands for authentication.
+
+ This function processes Ultralytics HUB CLI commands such as login and logout. It should be called when executing a
+ script with arguments related to HUB authentication.
+
+ Args:
+ args (List[str]): A list of command line arguments. The first argument should be either 'login'
+ or 'logout'. For 'login', an optional second argument can be the API key.
+
+ Examples:
+ ```bash
+ yolo login YOUR_API_KEY
+ ```
+
+ Notes:
+ - The function imports the 'hub' module from ultralytics to perform login and logout operations.
+ - For the 'login' command, if no API key is provided, an empty string is passed to the login function.
+ - The 'logout' command does not require any additional arguments.
+ """
+ from ultralytics import hub
+
+ if args[0] == "login":
+ key = args[1] if len(args) > 1 else ""
+ # Log in to Ultralytics HUB using the provided API key
+ hub.login(key)
+ elif args[0] == "logout":
+ # Log out from Ultralytics HUB
+ hub.logout()
+
+
+def handle_yolo_settings(args: List[str]) -> None:
+ """
+ Handles YOLO settings command-line interface (CLI) commands.
+
+ This function processes YOLO settings CLI commands such as reset and updating individual settings. It should be
+ called when executing a script with arguments related to YOLO settings management.
+
+ Args:
+ args (List[str]): A list of command line arguments for YOLO settings management.
+
+ Examples:
+ >>> handle_yolo_settings(["reset"]) # Reset YOLO settings
+ >>> handle_yolo_settings(["default_cfg_path=yolo11n.yaml"]) # Update a specific setting
+
+ Notes:
+ - If no arguments are provided, the function will display the current settings.
+ - The 'reset' command will delete the existing settings file and create new default settings.
+ - Other arguments are treated as key-value pairs to update specific settings.
+ - The function will check for alignment between the provided settings and the existing ones.
+ - After processing, the updated settings will be displayed.
+ - For more information on handling YOLO settings, visit:
+ https://docs.ultralytics.com/quickstart/#ultralytics-settings
+ """
+ url = "https://docs.ultralytics.com/quickstart/#ultralytics-settings" # help URL
+ try:
+ if any(args):
+ if args[0] == "reset":
+ SETTINGS_FILE.unlink() # delete the settings file
+ SETTINGS.reset() # create new settings
+ LOGGER.info("Settings reset successfully") # inform the user that settings have been reset
+ else: # save a new setting
+ new = dict(parse_key_value_pair(a) for a in args)
+ check_dict_alignment(SETTINGS, new)
+ SETTINGS.update(new)
+
+ print(SETTINGS) # print the current settings
+ LOGGER.info(f"💡 Learn more about Ultralytics Settings at {url}")
+ except Exception as e:
+ LOGGER.warning(f"WARNING ⚠️ settings error: '{e}'. Please see {url} for help.")
+
+
+def handle_yolo_solutions(args: List[str]) -> None:
+ """
+ Processes YOLO solutions arguments and runs the specified computer vision solutions pipeline.
+
+ Args:
+ args (List[str]): Command-line arguments for configuring and running the Ultralytics YOLO
+ solutions: https://docs.ultralytics.com/solutions/, It can include solution name, source,
+ and other configuration parameters.
+
+ Returns:
+ None: The function processes video frames and saves the output but doesn't return any value.
+
+ Examples:
+ Run people counting solution with default settings:
+ >>> handle_yolo_solutions(["count"])
+
+ Run analytics with custom configuration:
+ >>> handle_yolo_solutions(["analytics", "conf=0.25", "source=path/to/video/file.mp4"])
+
+ Run inference with custom configuration, requires Streamlit version 1.29.0 or higher.
+ >>> handle_yolo_solutions(["inference", "model=yolo11n.pt"])
+
+ Notes:
+ - Default configurations are merged from DEFAULT_SOL_DICT and DEFAULT_CFG_DICT
+ - Arguments can be provided in the format 'key=value' or as boolean flags
+ - Available solutions are defined in SOLUTION_MAP with their respective classes and methods
+ - If an invalid solution is provided, defaults to 'count' solution
+ - Output videos are saved in 'runs/solution/{solution_name}' directory
+ - For 'analytics' solution, frame numbers are tracked for generating analytical graphs
+ - Video processing can be interrupted by pressing 'q'
+ - Processes video frames sequentially and saves output in .avi format
+ - If no source is specified, downloads and uses a default sample video\
+ - The inference solution will be launched using the 'streamlit run' command.
+ - The Streamlit app file is located in the Ultralytics package directory.
+ """
+ full_args_dict = {**DEFAULT_SOL_DICT, **DEFAULT_CFG_DICT} # arguments dictionary
+ overrides = {}
+
+ # check dictionary alignment
+ for arg in merge_equals_args(args):
+ arg = arg.lstrip("-").rstrip(",")
+ if "=" in arg:
+ try:
+ k, v = parse_key_value_pair(arg)
+ overrides[k] = v
+ except (NameError, SyntaxError, ValueError, AssertionError) as e:
+ check_dict_alignment(full_args_dict, {arg: ""}, e)
+ elif arg in full_args_dict and isinstance(full_args_dict.get(arg), bool):
+ overrides[arg] = True
+ check_dict_alignment(full_args_dict, overrides) # dict alignment
+
+ # Get solution name
+ if args and args[0] in SOLUTION_MAP:
+ if args[0] != "help":
+ s_n = args.pop(0) # Extract the solution name directly
+ else:
+ LOGGER.info(SOLUTIONS_HELP_MSG)
+ else:
+ LOGGER.warning(
+ f"⚠️ No valid solution provided. Using default 'count'. Available: {', '.join(SOLUTION_MAP.keys())}"
+ )
+ s_n = "count" # Default solution if none provided
+
+ if args and args[0] == "help": # Add check for return if user call `yolo solutions help`
+ return
+
+ if s_n == "inference":
+ checks.check_requirements("streamlit>=1.29.0")
+ LOGGER.info("💡 Loading Ultralytics live inference app...")
+ subprocess.run(
+ [ # Run subprocess with Streamlit custom argument
+ "streamlit",
+ "run",
+ str(ROOT / "solutions/streamlit_inference.py"),
+ "--server.headless",
+ "true",
+ overrides.pop("model", "yolo11n.pt"),
+ ]
+ )
+ else:
+ cls, method = SOLUTION_MAP[s_n] # solution class name, method name and default source
+
+ from ultralytics import solutions # import ultralytics solutions
+
+ solution = getattr(solutions, cls)(IS_CLI=True, **overrides) # get solution class i.e ObjectCounter
+ process = getattr(
+ solution, method
+ ) # get specific function of class for processing i.e, count from ObjectCounter
+
+ cap = cv2.VideoCapture(solution.CFG["source"]) # read the video file
+
+ # extract width, height and fps of the video file, create save directory and initialize video writer
+ import os # for directory creation
+ from pathlib import Path
+
+ from ultralytics.utils.files import increment_path # for output directory path update
+
+ w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
+ if s_n == "analytics": # analytical graphs follow fixed shape for output i.e w=1920, h=1080
+ w, h = 1920, 1080
+ save_dir = increment_path(Path("runs") / "solutions" / "exp", exist_ok=False)
+ save_dir.mkdir(parents=True, exist_ok=True) # create the output directory
+ vw = cv2.VideoWriter(os.path.join(save_dir, "solution.avi"), cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
+
+ try: # Process video frames
+ f_n = 0 # frame number, required for analytical graphs
+ while cap.isOpened():
+ success, frame = cap.read()
+ if not success:
+ break
+ frame = process(frame, f_n := f_n + 1) if s_n == "analytics" else process(frame)
+ vw.write(frame)
+ if cv2.waitKey(1) & 0xFF == ord("q"):
+ break
+ finally:
+ cap.release()
+
+
+def parse_key_value_pair(pair: str = "key=value"):
+ """
+ Parses a key-value pair string into separate key and value components.
+
+ Args:
+ pair (str): A string containing a key-value pair in the format "key=value".
+
+ Returns:
+ key (str): The parsed key.
+ value (str): The parsed value.
+
+ Raises:
+ AssertionError: If the value is missing or empty.
+
+ Examples:
+ >>> key, value = parse_key_value_pair("model=yolo11n.pt")
+ >>> print(f"Key: {key}, Value: {value}")
+ Key: model, Value: yolo11n.pt
+
+ >>> key, value = parse_key_value_pair("epochs=100")
+ >>> print(f"Key: {key}, Value: {value}")
+ Key: epochs, Value: 100
+
+ Notes:
+ - The function splits the input string on the first '=' character.
+ - Leading and trailing whitespace is removed from both key and value.
+ - An assertion error is raised if the value is empty after stripping.
+ """
+ k, v = pair.split("=", 1) # split on first '=' sign
+ k, v = k.strip(), v.strip() # remove spaces
+ assert v, f"missing '{k}' value"
+ return k, smart_value(v)
+
+
+def smart_value(v):
+ """
+ Converts a string representation of a value to its appropriate Python type.
+
+ This function attempts to convert a given string into a Python object of the most appropriate type. It handles
+ conversions to None, bool, int, float, and other types that can be evaluated safely.
+
+ Args:
+ v (str): The string representation of the value to be converted.
+
+ Returns:
+ (Any): The converted value. The type can be None, bool, int, float, or the original string if no conversion
+ is applicable.
+
+ Examples:
+ >>> smart_value("42")
+ 42
+ >>> smart_value("3.14")
+ 3.14
+ >>> smart_value("True")
+ True
+ >>> smart_value("None")
+ None
+ >>> smart_value("some_string")
+ 'some_string'
+
+ Notes:
+ - The function uses a case-insensitive comparison for boolean and None values.
+ - For other types, it attempts to use Python's eval() function, which can be unsafe if used on untrusted input.
+ - If no conversion is possible, the original string is returned.
+ """
+ v_lower = v.lower()
+ if v_lower == "none":
+ return None
+ elif v_lower == "true":
+ return True
+ elif v_lower == "false":
+ return False
+ else:
+ try:
+ return eval(v)
+ except Exception:
+ return v
+
+
+def entrypoint(debug=""):
+ """
+ Ultralytics entrypoint function for parsing and executing command-line arguments.
+
+ This function serves as the main entry point for the Ultralytics CLI, parsing command-line arguments and
+ executing the corresponding tasks such as training, validation, prediction, exporting models, and more.
+
+ Args:
+ debug (str): Space-separated string of command-line arguments for debugging purposes.
+
+ Examples:
+ Train a detection model for 10 epochs with an initial learning_rate of 0.01:
+ >>> entrypoint("train data=coco8.yaml model=yolo11n.pt epochs=10 lr0=0.01")
+
+ Predict a YouTube video using a pretrained segmentation model at image size 320:
+ >>> entrypoint("predict model=yolo11n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320")
+
+ Validate a pretrained detection model at batch-size 1 and image size 640:
+ >>> entrypoint("val model=yolo11n.pt data=coco8.yaml batch=1 imgsz=640")
+
+ Notes:
+ - If no arguments are passed, the function will display the usage help message.
+ - For a list of all available commands and their arguments, see the provided help messages and the
+ Ultralytics documentation at https://docs.ultralytics.com.
+ """
+ args = (debug.split(" ") if debug else ARGV)[1:]
+ if not args: # no arguments passed
+ LOGGER.info(CLI_HELP_MSG)
+ return
+
+ special = {
+ "help": lambda: LOGGER.info(CLI_HELP_MSG),
+ "checks": checks.collect_system_info,
+ "version": lambda: LOGGER.info(__version__),
+ "settings": lambda: handle_yolo_settings(args[1:]),
+ "cfg": lambda: yaml_print(DEFAULT_CFG_PATH),
+ "hub": lambda: handle_yolo_hub(args[1:]),
+ "login": lambda: handle_yolo_hub(args),
+ "logout": lambda: handle_yolo_hub(args),
+ "copy-cfg": copy_default_cfg,
+ "solutions": lambda: handle_yolo_solutions(args[1:]),
+ }
+ full_args_dict = {**DEFAULT_CFG_DICT, **{k: None for k in TASKS}, **{k: None for k in MODES}, **special}
+
+ # Define common misuses of special commands, i.e. -h, -help, --help
+ special.update({k[0]: v for k, v in special.items()}) # singular
+ special.update({k[:-1]: v for k, v in special.items() if len(k) > 1 and k.endswith("s")}) # singular
+ special = {**special, **{f"-{k}": v for k, v in special.items()}, **{f"--{k}": v for k, v in special.items()}}
+
+ overrides = {} # basic overrides, i.e. imgsz=320
+ for a in merge_equals_args(args): # merge spaces around '=' sign
+ if a.startswith("--"):
+ LOGGER.warning(f"WARNING ⚠️ argument '{a}' does not require leading dashes '--', updating to '{a[2:]}'.")
+ a = a[2:]
+ if a.endswith(","):
+ LOGGER.warning(f"WARNING ⚠️ argument '{a}' does not require trailing comma ',', updating to '{a[:-1]}'.")
+ a = a[:-1]
+ if "=" in a:
+ try:
+ k, v = parse_key_value_pair(a)
+ if k == "cfg" and v is not None: # custom.yaml passed
+ LOGGER.info(f"Overriding {DEFAULT_CFG_PATH} with {v}")
+ overrides = {k: val for k, val in yaml_load(checks.check_yaml(v)).items() if k != "cfg"}
+ else:
+ overrides[k] = v
+ except (NameError, SyntaxError, ValueError, AssertionError) as e:
+ check_dict_alignment(full_args_dict, {a: ""}, e)
+
+ elif a in TASKS:
+ overrides["task"] = a
+ elif a in MODES:
+ overrides["mode"] = a
+ elif a.lower() in special:
+ special[a.lower()]()
+ return
+ elif a in DEFAULT_CFG_DICT and isinstance(DEFAULT_CFG_DICT[a], bool):
+ overrides[a] = True # auto-True for default bool args, i.e. 'yolo show' sets show=True
+ elif a in DEFAULT_CFG_DICT:
+ raise SyntaxError(
+ f"'{colorstr('red', 'bold', a)}' is a valid YOLO argument but is missing an '=' sign "
+ f"to set its value, i.e. try '{a}={DEFAULT_CFG_DICT[a]}'\n{CLI_HELP_MSG}"
+ )
+ else:
+ check_dict_alignment(full_args_dict, {a: ""})
+
+ # Check keys
+ check_dict_alignment(full_args_dict, overrides)
+
+ # Mode
+ mode = overrides.get("mode")
+ if mode is None:
+ mode = DEFAULT_CFG.mode or "predict"
+ LOGGER.warning(f"WARNING ⚠️ 'mode' argument is missing. Valid modes are {MODES}. Using default 'mode={mode}'.")
+ elif mode not in MODES:
+ raise ValueError(f"Invalid 'mode={mode}'. Valid modes are {MODES}.\n{CLI_HELP_MSG}")
+
+ # Task
+ task = overrides.pop("task", None)
+ if task:
+ if task == "classify" and mode == "track":
+ raise ValueError(
+ f"❌ Classification doesn't support 'mode=track'. Valid modes for classification are"
+ f" {MODES - {'track'}}.\n{CLI_HELP_MSG}"
+ )
+ elif task not in TASKS:
+ if task == "track":
+ LOGGER.warning(
+ "WARNING ⚠️ invalid 'task=track', setting 'task=detect' and 'mode=track'. Valid tasks are {TASKS}.\n{CLI_HELP_MSG}."
+ )
+ task, mode = "detect", "track"
+ else:
+ raise ValueError(f"Invalid 'task={task}'. Valid tasks are {TASKS}.\n{CLI_HELP_MSG}")
+ if "model" not in overrides:
+ overrides["model"] = TASK2MODEL[task]
+
+ # Model
+ model = overrides.pop("model", DEFAULT_CFG.model)
+ if model is None:
+ model = "yolo11n.pt"
+ LOGGER.warning(f"WARNING ⚠️ 'model' argument is missing. Using default 'model={model}'.")
+ overrides["model"] = model
+ stem = Path(model).stem.lower()
+ if "rtdetr" in stem: # guess architecture
+ from ultralytics import RTDETR
+
+ model = RTDETR(model) # no task argument
+ elif "fastsam" in stem:
+ from ultralytics import FastSAM
+
+ model = FastSAM(model)
+ elif "sam_" in stem or "sam2_" in stem or "sam2.1_" in stem:
+ from ultralytics import SAM
+
+ model = SAM(model)
+ else:
+ from ultralytics import YOLO
+
+ model = YOLO(model, task=task)
+ if isinstance(overrides.get("pretrained"), str):
+ model.load(overrides["pretrained"])
+
+ # Task Update
+ if task != model.task:
+ if task:
+ LOGGER.warning(
+ f"WARNING ⚠️ conflicting 'task={task}' passed with 'task={model.task}' model. "
+ f"Ignoring 'task={task}' and updating to 'task={model.task}' to match model."
+ )
+ task = model.task
+
+ # Mode
+ if mode in {"predict", "track"} and "source" not in overrides:
+ overrides["source"] = (
+ "https://ultralytics.com/images/boats.jpg" if task == "obb" else DEFAULT_CFG.source or ASSETS
+ )
+ LOGGER.warning(f"WARNING ⚠️ 'source' argument is missing. Using default 'source={overrides['source']}'.")
+ elif mode in {"train", "val"}:
+ if "data" not in overrides and "resume" not in overrides:
+ overrides["data"] = DEFAULT_CFG.data or TASK2DATA.get(task or DEFAULT_CFG.task, DEFAULT_CFG.data)
+ LOGGER.warning(f"WARNING ⚠️ 'data' argument is missing. Using default 'data={overrides['data']}'.")
+ elif mode == "export":
+ if "format" not in overrides:
+ overrides["format"] = DEFAULT_CFG.format or "torchscript"
+ LOGGER.warning(f"WARNING ⚠️ 'format' argument is missing. Using default 'format={overrides['format']}'.")
+
+ # Run command in python
+ getattr(model, mode)(**overrides) # default args from model
+
+ # Show help
+ LOGGER.info(f"💡 Learn more at https://docs.ultralytics.com/modes/{mode}")
+
+ # Recommend VS Code extension
+ if IS_VSCODE and SETTINGS.get("vscode_msg", True):
+ LOGGER.info(vscode_msg())
+
+
+# Special modes --------------------------------------------------------------------------------------------------------
+def copy_default_cfg():
+ """
+ Copies the default configuration file and creates a new one with '_copy' appended to its name.
+
+ This function duplicates the existing default configuration file (DEFAULT_CFG_PATH) and saves it
+ with '_copy' appended to its name in the current working directory. It provides a convenient way
+ to create a custom configuration file based on the default settings.
+
+ Examples:
+ >>> copy_default_cfg()
+ # Output: default.yaml copied to /path/to/current/directory/default_copy.yaml
+ # Example YOLO command with this new custom cfg:
+ # yolo cfg='/path/to/current/directory/default_copy.yaml' imgsz=320 batch=8
+
+ Notes:
+ - The new configuration file is created in the current working directory.
+ - After copying, the function prints a message with the new file's location and an example
+ YOLO command demonstrating how to use the new configuration file.
+ - This function is useful for users who want to modify the default configuration without
+ altering the original file.
+ """
+ new_file = Path.cwd() / DEFAULT_CFG_PATH.name.replace(".yaml", "_copy.yaml")
+ shutil.copy2(DEFAULT_CFG_PATH, new_file)
+ LOGGER.info(
+ f"{DEFAULT_CFG_PATH} copied to {new_file}\n"
+ f"Example YOLO command with this new custom cfg:\n yolo cfg='{new_file}' imgsz=320 batch=8"
+ )
+
+
+if __name__ == "__main__":
+ # Example: entrypoint(debug='yolo predict model=yolo11n.pt')
+ entrypoint(debug="")
diff --git a/ultralytics/cfg/datasets/Argoverse.yaml b/ultralytics/cfg/datasets/Argoverse.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5e05023d7797ca6e7221c4226fcc3e552bd3edb3
--- /dev/null
+++ b/ultralytics/cfg/datasets/Argoverse.yaml
@@ -0,0 +1,75 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Argoverse-HD dataset (ring-front-center camera) https://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
+# Documentation: https://docs.ultralytics.com/datasets/detect/argoverse/
+# Example usage: yolo train data=Argoverse.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── Argoverse ← downloads here (31.5 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/Argoverse # dataset root dir
+train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
+val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
+test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
+
+# Classes
+names:
+ 0: person
+ 1: bicycle
+ 2: car
+ 3: motorcycle
+ 4: bus
+ 5: truck
+ 6: traffic_light
+ 7: stop_sign
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ import json
+ from tqdm import tqdm
+ from ultralytics.utils.downloads import download
+ from pathlib import Path
+
+ def argoverse2yolo(set):
+ labels = {}
+ a = json.load(open(set, "rb"))
+ for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."):
+ img_id = annot['image_id']
+ img_name = a['images'][img_id]['name']
+ img_label_name = f'{img_name[:-3]}txt'
+
+ cls = annot['category_id'] # instance class id
+ x_center, y_center, width, height = annot['bbox']
+ x_center = (x_center + width / 2) / 1920.0 # offset and scale
+ y_center = (y_center + height / 2) / 1200.0 # offset and scale
+ width /= 1920.0 # scale
+ height /= 1200.0 # scale
+
+ img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]
+ if not img_dir.exists():
+ img_dir.mkdir(parents=True, exist_ok=True)
+
+ k = str(img_dir / img_label_name)
+ if k not in labels:
+ labels[k] = []
+ labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
+
+ for k in labels:
+ with open(k, "w") as f:
+ f.writelines(labels[k])
+
+
+ # Download 'https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip' (deprecated S3 link)
+ dir = Path(yaml['path']) # dataset root dir
+ urls = ['https://drive.google.com/file/d/1st9qW3BeIwQsnR0t8mRpvbsSWIo16ACi/view?usp=drive_link']
+ print("\n\nWARNING: Argoverse dataset MUST be downloaded manually, autodownload will NOT work.")
+ print(f"WARNING: Manually download Argoverse dataset '{urls[0]}' to '{dir}' and re-run your command.\n\n")
+ # download(urls, dir=dir)
+
+ # Convert
+ annotations_dir = 'Argoverse-HD/annotations/'
+ (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images'
+ for d in "train.json", "val.json":
+ argoverse2yolo(dir / annotations_dir / d) # convert Argoverse annotations to YOLO labels
diff --git a/ultralytics/cfg/datasets/DOTAv1.5.yaml b/ultralytics/cfg/datasets/DOTAv1.5.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..26c73808d7b253dea0b4555394bae246a815076d
--- /dev/null
+++ b/ultralytics/cfg/datasets/DOTAv1.5.yaml
@@ -0,0 +1,37 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# DOTA 1.5 dataset https://captain-whu.github.io/DOTA/index.html for object detection in aerial images by Wuhan University
+# Documentation: https://docs.ultralytics.com/datasets/obb/dota-v2/
+# Example usage: yolo train model=yolov8n-obb.pt data=DOTAv1.5.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── dota1.5 ← downloads here (2GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/DOTAv1.5 # dataset root dir
+train: images/train # train images (relative to 'path') 1411 images
+val: images/val # val images (relative to 'path') 458 images
+test: images/test # test images (optional) 937 images
+
+# Classes for DOTA 1.5
+names:
+ 0: plane
+ 1: ship
+ 2: storage tank
+ 3: baseball diamond
+ 4: tennis court
+ 5: basketball court
+ 6: ground track field
+ 7: harbor
+ 8: bridge
+ 9: large vehicle
+ 10: small vehicle
+ 11: helicopter
+ 12: roundabout
+ 13: soccer ball field
+ 14: swimming pool
+ 15: container crane
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/DOTAv1.5.zip
diff --git a/ultralytics/cfg/datasets/DOTAv1.yaml b/ultralytics/cfg/datasets/DOTAv1.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5e71d2188d50d3efc80562ebb3fa7b65e07d2b5f
--- /dev/null
+++ b/ultralytics/cfg/datasets/DOTAv1.yaml
@@ -0,0 +1,36 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# DOTA 1.0 dataset https://captain-whu.github.io/DOTA/index.html for object detection in aerial images by Wuhan University
+# Documentation: https://docs.ultralytics.com/datasets/obb/dota-v2/
+# Example usage: yolo train model=yolov8n-obb.pt data=DOTAv1.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── dota1 ← downloads here (2GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/DOTAv1 # dataset root dir
+train: images/train # train images (relative to 'path') 1411 images
+val: images/val # val images (relative to 'path') 458 images
+test: images/test # test images (optional) 937 images
+
+# Classes for DOTA 1.0
+names:
+ 0: plane
+ 1: ship
+ 2: storage tank
+ 3: baseball diamond
+ 4: tennis court
+ 5: basketball court
+ 6: ground track field
+ 7: harbor
+ 8: bridge
+ 9: large vehicle
+ 10: small vehicle
+ 11: helicopter
+ 12: roundabout
+ 13: soccer ball field
+ 14: swimming pool
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/DOTAv1.zip
diff --git a/ultralytics/cfg/datasets/GlobalWheat2020.yaml b/ultralytics/cfg/datasets/GlobalWheat2020.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9dff73d7cd2c9be88e7d936208f649dc306cbdf9
--- /dev/null
+++ b/ultralytics/cfg/datasets/GlobalWheat2020.yaml
@@ -0,0 +1,54 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Global Wheat 2020 dataset https://www.global-wheat.com/ by University of Saskatchewan
+# Documentation: https://docs.ultralytics.com/datasets/detect/globalwheat2020/
+# Example usage: yolo train data=GlobalWheat2020.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── GlobalWheat2020 ← downloads here (7.0 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/GlobalWheat2020 # dataset root dir
+train: # train images (relative to 'path') 3422 images
+ - images/arvalis_1
+ - images/arvalis_2
+ - images/arvalis_3
+ - images/ethz_1
+ - images/rres_1
+ - images/inrae_1
+ - images/usask_1
+val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
+ - images/ethz_1
+test: # test images (optional) 1276 images
+ - images/utokyo_1
+ - images/utokyo_2
+ - images/nau_1
+ - images/uq_1
+
+# Classes
+names:
+ 0: wheat_head
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ from ultralytics.utils.downloads import download
+ from pathlib import Path
+
+ # Download
+ dir = Path(yaml['path']) # dataset root dir
+ urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
+ 'https://github.com/ultralytics/assets/releases/download/v0.0.0/GlobalWheat2020_labels.zip']
+ download(urls, dir=dir)
+
+ # Make Directories
+ for p in 'annotations', 'images', 'labels':
+ (dir / p).mkdir(parents=True, exist_ok=True)
+
+ # Move
+ for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
+ 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
+ (dir / 'global-wheat-codalab-official' / p).rename(dir / 'images' / p) # move to /images
+ f = (dir / 'global-wheat-codalab-official' / p).with_suffix('.json') # json file
+ if f.exists():
+ f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
diff --git a/ultralytics/cfg/datasets/ImageNet.yaml b/ultralytics/cfg/datasets/ImageNet.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..92e398a8fa846a8921e11078b277c748967f1ec6
--- /dev/null
+++ b/ultralytics/cfg/datasets/ImageNet.yaml
@@ -0,0 +1,2025 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
+# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
+# Documentation: https://docs.ultralytics.com/datasets/classify/imagenet/
+# Example usage: yolo train task=classify data=imagenet
+# parent
+# ├── ultralytics
+# └── datasets
+# └── imagenet ← downloads here (144 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/imagenet # dataset root dir
+train: train # train images (relative to 'path') 1281167 images
+val: val # val images (relative to 'path') 50000 images
+test: # test images (optional)
+
+# Classes
+names:
+ 0: tench
+ 1: goldfish
+ 2: great white shark
+ 3: tiger shark
+ 4: hammerhead shark
+ 5: electric ray
+ 6: stingray
+ 7: cock
+ 8: hen
+ 9: ostrich
+ 10: brambling
+ 11: goldfinch
+ 12: house finch
+ 13: junco
+ 14: indigo bunting
+ 15: American robin
+ 16: bulbul
+ 17: jay
+ 18: magpie
+ 19: chickadee
+ 20: American dipper
+ 21: kite
+ 22: bald eagle
+ 23: vulture
+ 24: great grey owl
+ 25: fire salamander
+ 26: smooth newt
+ 27: newt
+ 28: spotted salamander
+ 29: axolotl
+ 30: American bullfrog
+ 31: tree frog
+ 32: tailed frog
+ 33: loggerhead sea turtle
+ 34: leatherback sea turtle
+ 35: mud turtle
+ 36: terrapin
+ 37: box turtle
+ 38: banded gecko
+ 39: green iguana
+ 40: Carolina anole
+ 41: desert grassland whiptail lizard
+ 42: agama
+ 43: frilled-necked lizard
+ 44: alligator lizard
+ 45: Gila monster
+ 46: European green lizard
+ 47: chameleon
+ 48: Komodo dragon
+ 49: Nile crocodile
+ 50: American alligator
+ 51: triceratops
+ 52: worm snake
+ 53: ring-necked snake
+ 54: eastern hog-nosed snake
+ 55: smooth green snake
+ 56: kingsnake
+ 57: garter snake
+ 58: water snake
+ 59: vine snake
+ 60: night snake
+ 61: boa constrictor
+ 62: African rock python
+ 63: Indian cobra
+ 64: green mamba
+ 65: sea snake
+ 66: Saharan horned viper
+ 67: eastern diamondback rattlesnake
+ 68: sidewinder
+ 69: trilobite
+ 70: harvestman
+ 71: scorpion
+ 72: yellow garden spider
+ 73: barn spider
+ 74: European garden spider
+ 75: southern black widow
+ 76: tarantula
+ 77: wolf spider
+ 78: tick
+ 79: centipede
+ 80: black grouse
+ 81: ptarmigan
+ 82: ruffed grouse
+ 83: prairie grouse
+ 84: peacock
+ 85: quail
+ 86: partridge
+ 87: grey parrot
+ 88: macaw
+ 89: sulphur-crested cockatoo
+ 90: lorikeet
+ 91: coucal
+ 92: bee eater
+ 93: hornbill
+ 94: hummingbird
+ 95: jacamar
+ 96: toucan
+ 97: duck
+ 98: red-breasted merganser
+ 99: goose
+ 100: black swan
+ 101: tusker
+ 102: echidna
+ 103: platypus
+ 104: wallaby
+ 105: koala
+ 106: wombat
+ 107: jellyfish
+ 108: sea anemone
+ 109: brain coral
+ 110: flatworm
+ 111: nematode
+ 112: conch
+ 113: snail
+ 114: slug
+ 115: sea slug
+ 116: chiton
+ 117: chambered nautilus
+ 118: Dungeness crab
+ 119: rock crab
+ 120: fiddler crab
+ 121: red king crab
+ 122: American lobster
+ 123: spiny lobster
+ 124: crayfish
+ 125: hermit crab
+ 126: isopod
+ 127: white stork
+ 128: black stork
+ 129: spoonbill
+ 130: flamingo
+ 131: little blue heron
+ 132: great egret
+ 133: bittern
+ 134: crane (bird)
+ 135: limpkin
+ 136: common gallinule
+ 137: American coot
+ 138: bustard
+ 139: ruddy turnstone
+ 140: dunlin
+ 141: common redshank
+ 142: dowitcher
+ 143: oystercatcher
+ 144: pelican
+ 145: king penguin
+ 146: albatross
+ 147: grey whale
+ 148: killer whale
+ 149: dugong
+ 150: sea lion
+ 151: Chihuahua
+ 152: Japanese Chin
+ 153: Maltese
+ 154: Pekingese
+ 155: Shih Tzu
+ 156: King Charles Spaniel
+ 157: Papillon
+ 158: toy terrier
+ 159: Rhodesian Ridgeback
+ 160: Afghan Hound
+ 161: Basset Hound
+ 162: Beagle
+ 163: Bloodhound
+ 164: Bluetick Coonhound
+ 165: Black and Tan Coonhound
+ 166: Treeing Walker Coonhound
+ 167: English foxhound
+ 168: Redbone Coonhound
+ 169: borzoi
+ 170: Irish Wolfhound
+ 171: Italian Greyhound
+ 172: Whippet
+ 173: Ibizan Hound
+ 174: Norwegian Elkhound
+ 175: Otterhound
+ 176: Saluki
+ 177: Scottish Deerhound
+ 178: Weimaraner
+ 179: Staffordshire Bull Terrier
+ 180: American Staffordshire Terrier
+ 181: Bedlington Terrier
+ 182: Border Terrier
+ 183: Kerry Blue Terrier
+ 184: Irish Terrier
+ 185: Norfolk Terrier
+ 186: Norwich Terrier
+ 187: Yorkshire Terrier
+ 188: Wire Fox Terrier
+ 189: Lakeland Terrier
+ 190: Sealyham Terrier
+ 191: Airedale Terrier
+ 192: Cairn Terrier
+ 193: Australian Terrier
+ 194: Dandie Dinmont Terrier
+ 195: Boston Terrier
+ 196: Miniature Schnauzer
+ 197: Giant Schnauzer
+ 198: Standard Schnauzer
+ 199: Scottish Terrier
+ 200: Tibetan Terrier
+ 201: Australian Silky Terrier
+ 202: Soft-coated Wheaten Terrier
+ 203: West Highland White Terrier
+ 204: Lhasa Apso
+ 205: Flat-Coated Retriever
+ 206: Curly-coated Retriever
+ 207: Golden Retriever
+ 208: Labrador Retriever
+ 209: Chesapeake Bay Retriever
+ 210: German Shorthaired Pointer
+ 211: Vizsla
+ 212: English Setter
+ 213: Irish Setter
+ 214: Gordon Setter
+ 215: Brittany
+ 216: Clumber Spaniel
+ 217: English Springer Spaniel
+ 218: Welsh Springer Spaniel
+ 219: Cocker Spaniels
+ 220: Sussex Spaniel
+ 221: Irish Water Spaniel
+ 222: Kuvasz
+ 223: Schipperke
+ 224: Groenendael
+ 225: Malinois
+ 226: Briard
+ 227: Australian Kelpie
+ 228: Komondor
+ 229: Old English Sheepdog
+ 230: Shetland Sheepdog
+ 231: collie
+ 232: Border Collie
+ 233: Bouvier des Flandres
+ 234: Rottweiler
+ 235: German Shepherd Dog
+ 236: Dobermann
+ 237: Miniature Pinscher
+ 238: Greater Swiss Mountain Dog
+ 239: Bernese Mountain Dog
+ 240: Appenzeller Sennenhund
+ 241: Entlebucher Sennenhund
+ 242: Boxer
+ 243: Bullmastiff
+ 244: Tibetan Mastiff
+ 245: French Bulldog
+ 246: Great Dane
+ 247: St. Bernard
+ 248: husky
+ 249: Alaskan Malamute
+ 250: Siberian Husky
+ 251: Dalmatian
+ 252: Affenpinscher
+ 253: Basenji
+ 254: pug
+ 255: Leonberger
+ 256: Newfoundland
+ 257: Pyrenean Mountain Dog
+ 258: Samoyed
+ 259: Pomeranian
+ 260: Chow Chow
+ 261: Keeshond
+ 262: Griffon Bruxellois
+ 263: Pembroke Welsh Corgi
+ 264: Cardigan Welsh Corgi
+ 265: Toy Poodle
+ 266: Miniature Poodle
+ 267: Standard Poodle
+ 268: Mexican hairless dog
+ 269: grey wolf
+ 270: Alaskan tundra wolf
+ 271: red wolf
+ 272: coyote
+ 273: dingo
+ 274: dhole
+ 275: African wild dog
+ 276: hyena
+ 277: red fox
+ 278: kit fox
+ 279: Arctic fox
+ 280: grey fox
+ 281: tabby cat
+ 282: tiger cat
+ 283: Persian cat
+ 284: Siamese cat
+ 285: Egyptian Mau
+ 286: cougar
+ 287: lynx
+ 288: leopard
+ 289: snow leopard
+ 290: jaguar
+ 291: lion
+ 292: tiger
+ 293: cheetah
+ 294: brown bear
+ 295: American black bear
+ 296: polar bear
+ 297: sloth bear
+ 298: mongoose
+ 299: meerkat
+ 300: tiger beetle
+ 301: ladybug
+ 302: ground beetle
+ 303: longhorn beetle
+ 304: leaf beetle
+ 305: dung beetle
+ 306: rhinoceros beetle
+ 307: weevil
+ 308: fly
+ 309: bee
+ 310: ant
+ 311: grasshopper
+ 312: cricket
+ 313: stick insect
+ 314: cockroach
+ 315: mantis
+ 316: cicada
+ 317: leafhopper
+ 318: lacewing
+ 319: dragonfly
+ 320: damselfly
+ 321: red admiral
+ 322: ringlet
+ 323: monarch butterfly
+ 324: small white
+ 325: sulphur butterfly
+ 326: gossamer-winged butterfly
+ 327: starfish
+ 328: sea urchin
+ 329: sea cucumber
+ 330: cottontail rabbit
+ 331: hare
+ 332: Angora rabbit
+ 333: hamster
+ 334: porcupine
+ 335: fox squirrel
+ 336: marmot
+ 337: beaver
+ 338: guinea pig
+ 339: common sorrel
+ 340: zebra
+ 341: pig
+ 342: wild boar
+ 343: warthog
+ 344: hippopotamus
+ 345: ox
+ 346: water buffalo
+ 347: bison
+ 348: ram
+ 349: bighorn sheep
+ 350: Alpine ibex
+ 351: hartebeest
+ 352: impala
+ 353: gazelle
+ 354: dromedary
+ 355: llama
+ 356: weasel
+ 357: mink
+ 358: European polecat
+ 359: black-footed ferret
+ 360: otter
+ 361: skunk
+ 362: badger
+ 363: armadillo
+ 364: three-toed sloth
+ 365: orangutan
+ 366: gorilla
+ 367: chimpanzee
+ 368: gibbon
+ 369: siamang
+ 370: guenon
+ 371: patas monkey
+ 372: baboon
+ 373: macaque
+ 374: langur
+ 375: black-and-white colobus
+ 376: proboscis monkey
+ 377: marmoset
+ 378: white-headed capuchin
+ 379: howler monkey
+ 380: titi
+ 381: Geoffroy's spider monkey
+ 382: common squirrel monkey
+ 383: ring-tailed lemur
+ 384: indri
+ 385: Asian elephant
+ 386: African bush elephant
+ 387: red panda
+ 388: giant panda
+ 389: snoek
+ 390: eel
+ 391: coho salmon
+ 392: rock beauty
+ 393: clownfish
+ 394: sturgeon
+ 395: garfish
+ 396: lionfish
+ 397: pufferfish
+ 398: abacus
+ 399: abaya
+ 400: academic gown
+ 401: accordion
+ 402: acoustic guitar
+ 403: aircraft carrier
+ 404: airliner
+ 405: airship
+ 406: altar
+ 407: ambulance
+ 408: amphibious vehicle
+ 409: analog clock
+ 410: apiary
+ 411: apron
+ 412: waste container
+ 413: assault rifle
+ 414: backpack
+ 415: bakery
+ 416: balance beam
+ 417: balloon
+ 418: ballpoint pen
+ 419: Band-Aid
+ 420: banjo
+ 421: baluster
+ 422: barbell
+ 423: barber chair
+ 424: barbershop
+ 425: barn
+ 426: barometer
+ 427: barrel
+ 428: wheelbarrow
+ 429: baseball
+ 430: basketball
+ 431: bassinet
+ 432: bassoon
+ 433: swimming cap
+ 434: bath towel
+ 435: bathtub
+ 436: station wagon
+ 437: lighthouse
+ 438: beaker
+ 439: military cap
+ 440: beer bottle
+ 441: beer glass
+ 442: bell-cot
+ 443: bib
+ 444: tandem bicycle
+ 445: bikini
+ 446: ring binder
+ 447: binoculars
+ 448: birdhouse
+ 449: boathouse
+ 450: bobsleigh
+ 451: bolo tie
+ 452: poke bonnet
+ 453: bookcase
+ 454: bookstore
+ 455: bottle cap
+ 456: bow
+ 457: bow tie
+ 458: brass
+ 459: bra
+ 460: breakwater
+ 461: breastplate
+ 462: broom
+ 463: bucket
+ 464: buckle
+ 465: bulletproof vest
+ 466: high-speed train
+ 467: butcher shop
+ 468: taxicab
+ 469: cauldron
+ 470: candle
+ 471: cannon
+ 472: canoe
+ 473: can opener
+ 474: cardigan
+ 475: car mirror
+ 476: carousel
+ 477: tool kit
+ 478: carton
+ 479: car wheel
+ 480: automated teller machine
+ 481: cassette
+ 482: cassette player
+ 483: castle
+ 484: catamaran
+ 485: CD player
+ 486: cello
+ 487: mobile phone
+ 488: chain
+ 489: chain-link fence
+ 490: chain mail
+ 491: chainsaw
+ 492: chest
+ 493: chiffonier
+ 494: chime
+ 495: china cabinet
+ 496: Christmas stocking
+ 497: church
+ 498: movie theater
+ 499: cleaver
+ 500: cliff dwelling
+ 501: cloak
+ 502: clogs
+ 503: cocktail shaker
+ 504: coffee mug
+ 505: coffeemaker
+ 506: coil
+ 507: combination lock
+ 508: computer keyboard
+ 509: confectionery store
+ 510: container ship
+ 511: convertible
+ 512: corkscrew
+ 513: cornet
+ 514: cowboy boot
+ 515: cowboy hat
+ 516: cradle
+ 517: crane (machine)
+ 518: crash helmet
+ 519: crate
+ 520: infant bed
+ 521: Crock Pot
+ 522: croquet ball
+ 523: crutch
+ 524: cuirass
+ 525: dam
+ 526: desk
+ 527: desktop computer
+ 528: rotary dial telephone
+ 529: diaper
+ 530: digital clock
+ 531: digital watch
+ 532: dining table
+ 533: dishcloth
+ 534: dishwasher
+ 535: disc brake
+ 536: dock
+ 537: dog sled
+ 538: dome
+ 539: doormat
+ 540: drilling rig
+ 541: drum
+ 542: drumstick
+ 543: dumbbell
+ 544: Dutch oven
+ 545: electric fan
+ 546: electric guitar
+ 547: electric locomotive
+ 548: entertainment center
+ 549: envelope
+ 550: espresso machine
+ 551: face powder
+ 552: feather boa
+ 553: filing cabinet
+ 554: fireboat
+ 555: fire engine
+ 556: fire screen sheet
+ 557: flagpole
+ 558: flute
+ 559: folding chair
+ 560: football helmet
+ 561: forklift
+ 562: fountain
+ 563: fountain pen
+ 564: four-poster bed
+ 565: freight car
+ 566: French horn
+ 567: frying pan
+ 568: fur coat
+ 569: garbage truck
+ 570: gas mask
+ 571: gas pump
+ 572: goblet
+ 573: go-kart
+ 574: golf ball
+ 575: golf cart
+ 576: gondola
+ 577: gong
+ 578: gown
+ 579: grand piano
+ 580: greenhouse
+ 581: grille
+ 582: grocery store
+ 583: guillotine
+ 584: barrette
+ 585: hair spray
+ 586: half-track
+ 587: hammer
+ 588: hamper
+ 589: hair dryer
+ 590: hand-held computer
+ 591: handkerchief
+ 592: hard disk drive
+ 593: harmonica
+ 594: harp
+ 595: harvester
+ 596: hatchet
+ 597: holster
+ 598: home theater
+ 599: honeycomb
+ 600: hook
+ 601: hoop skirt
+ 602: horizontal bar
+ 603: horse-drawn vehicle
+ 604: hourglass
+ 605: iPod
+ 606: clothes iron
+ 607: jack-o'-lantern
+ 608: jeans
+ 609: jeep
+ 610: T-shirt
+ 611: jigsaw puzzle
+ 612: pulled rickshaw
+ 613: joystick
+ 614: kimono
+ 615: knee pad
+ 616: knot
+ 617: lab coat
+ 618: ladle
+ 619: lampshade
+ 620: laptop computer
+ 621: lawn mower
+ 622: lens cap
+ 623: paper knife
+ 624: library
+ 625: lifeboat
+ 626: lighter
+ 627: limousine
+ 628: ocean liner
+ 629: lipstick
+ 630: slip-on shoe
+ 631: lotion
+ 632: speaker
+ 633: loupe
+ 634: sawmill
+ 635: magnetic compass
+ 636: mail bag
+ 637: mailbox
+ 638: tights
+ 639: tank suit
+ 640: manhole cover
+ 641: maraca
+ 642: marimba
+ 643: mask
+ 644: match
+ 645: maypole
+ 646: maze
+ 647: measuring cup
+ 648: medicine chest
+ 649: megalith
+ 650: microphone
+ 651: microwave oven
+ 652: military uniform
+ 653: milk can
+ 654: minibus
+ 655: miniskirt
+ 656: minivan
+ 657: missile
+ 658: mitten
+ 659: mixing bowl
+ 660: mobile home
+ 661: Model T
+ 662: modem
+ 663: monastery
+ 664: monitor
+ 665: moped
+ 666: mortar
+ 667: square academic cap
+ 668: mosque
+ 669: mosquito net
+ 670: scooter
+ 671: mountain bike
+ 672: tent
+ 673: computer mouse
+ 674: mousetrap
+ 675: moving van
+ 676: muzzle
+ 677: nail
+ 678: neck brace
+ 679: necklace
+ 680: nipple
+ 681: notebook computer
+ 682: obelisk
+ 683: oboe
+ 684: ocarina
+ 685: odometer
+ 686: oil filter
+ 687: organ
+ 688: oscilloscope
+ 689: overskirt
+ 690: bullock cart
+ 691: oxygen mask
+ 692: packet
+ 693: paddle
+ 694: paddle wheel
+ 695: padlock
+ 696: paintbrush
+ 697: pajamas
+ 698: palace
+ 699: pan flute
+ 700: paper towel
+ 701: parachute
+ 702: parallel bars
+ 703: park bench
+ 704: parking meter
+ 705: passenger car
+ 706: patio
+ 707: payphone
+ 708: pedestal
+ 709: pencil case
+ 710: pencil sharpener
+ 711: perfume
+ 712: Petri dish
+ 713: photocopier
+ 714: plectrum
+ 715: Pickelhaube
+ 716: picket fence
+ 717: pickup truck
+ 718: pier
+ 719: piggy bank
+ 720: pill bottle
+ 721: pillow
+ 722: ping-pong ball
+ 723: pinwheel
+ 724: pirate ship
+ 725: pitcher
+ 726: hand plane
+ 727: planetarium
+ 728: plastic bag
+ 729: plate rack
+ 730: plow
+ 731: plunger
+ 732: Polaroid camera
+ 733: pole
+ 734: police van
+ 735: poncho
+ 736: billiard table
+ 737: soda bottle
+ 738: pot
+ 739: potter's wheel
+ 740: power drill
+ 741: prayer rug
+ 742: printer
+ 743: prison
+ 744: projectile
+ 745: projector
+ 746: hockey puck
+ 747: punching bag
+ 748: purse
+ 749: quill
+ 750: quilt
+ 751: race car
+ 752: racket
+ 753: radiator
+ 754: radio
+ 755: radio telescope
+ 756: rain barrel
+ 757: recreational vehicle
+ 758: reel
+ 759: reflex camera
+ 760: refrigerator
+ 761: remote control
+ 762: restaurant
+ 763: revolver
+ 764: rifle
+ 765: rocking chair
+ 766: rotisserie
+ 767: eraser
+ 768: rugby ball
+ 769: ruler
+ 770: running shoe
+ 771: safe
+ 772: safety pin
+ 773: salt shaker
+ 774: sandal
+ 775: sarong
+ 776: saxophone
+ 777: scabbard
+ 778: weighing scale
+ 779: school bus
+ 780: schooner
+ 781: scoreboard
+ 782: CRT screen
+ 783: screw
+ 784: screwdriver
+ 785: seat belt
+ 786: sewing machine
+ 787: shield
+ 788: shoe store
+ 789: shoji
+ 790: shopping basket
+ 791: shopping cart
+ 792: shovel
+ 793: shower cap
+ 794: shower curtain
+ 795: ski
+ 796: ski mask
+ 797: sleeping bag
+ 798: slide rule
+ 799: sliding door
+ 800: slot machine
+ 801: snorkel
+ 802: snowmobile
+ 803: snowplow
+ 804: soap dispenser
+ 805: soccer ball
+ 806: sock
+ 807: solar thermal collector
+ 808: sombrero
+ 809: soup bowl
+ 810: space bar
+ 811: space heater
+ 812: space shuttle
+ 813: spatula
+ 814: motorboat
+ 815: spider web
+ 816: spindle
+ 817: sports car
+ 818: spotlight
+ 819: stage
+ 820: steam locomotive
+ 821: through arch bridge
+ 822: steel drum
+ 823: stethoscope
+ 824: scarf
+ 825: stone wall
+ 826: stopwatch
+ 827: stove
+ 828: strainer
+ 829: tram
+ 830: stretcher
+ 831: couch
+ 832: stupa
+ 833: submarine
+ 834: suit
+ 835: sundial
+ 836: sunglass
+ 837: sunglasses
+ 838: sunscreen
+ 839: suspension bridge
+ 840: mop
+ 841: sweatshirt
+ 842: swimsuit
+ 843: swing
+ 844: switch
+ 845: syringe
+ 846: table lamp
+ 847: tank
+ 848: tape player
+ 849: teapot
+ 850: teddy bear
+ 851: television
+ 852: tennis ball
+ 853: thatched roof
+ 854: front curtain
+ 855: thimble
+ 856: threshing machine
+ 857: throne
+ 858: tile roof
+ 859: toaster
+ 860: tobacco shop
+ 861: toilet seat
+ 862: torch
+ 863: totem pole
+ 864: tow truck
+ 865: toy store
+ 866: tractor
+ 867: semi-trailer truck
+ 868: tray
+ 869: trench coat
+ 870: tricycle
+ 871: trimaran
+ 872: tripod
+ 873: triumphal arch
+ 874: trolleybus
+ 875: trombone
+ 876: tub
+ 877: turnstile
+ 878: typewriter keyboard
+ 879: umbrella
+ 880: unicycle
+ 881: upright piano
+ 882: vacuum cleaner
+ 883: vase
+ 884: vault
+ 885: velvet
+ 886: vending machine
+ 887: vestment
+ 888: viaduct
+ 889: violin
+ 890: volleyball
+ 891: waffle iron
+ 892: wall clock
+ 893: wallet
+ 894: wardrobe
+ 895: military aircraft
+ 896: sink
+ 897: washing machine
+ 898: water bottle
+ 899: water jug
+ 900: water tower
+ 901: whiskey jug
+ 902: whistle
+ 903: wig
+ 904: window screen
+ 905: window shade
+ 906: Windsor tie
+ 907: wine bottle
+ 908: wing
+ 909: wok
+ 910: wooden spoon
+ 911: wool
+ 912: split-rail fence
+ 913: shipwreck
+ 914: yawl
+ 915: yurt
+ 916: website
+ 917: comic book
+ 918: crossword
+ 919: traffic sign
+ 920: traffic light
+ 921: dust jacket
+ 922: menu
+ 923: plate
+ 924: guacamole
+ 925: consomme
+ 926: hot pot
+ 927: trifle
+ 928: ice cream
+ 929: ice pop
+ 930: baguette
+ 931: bagel
+ 932: pretzel
+ 933: cheeseburger
+ 934: hot dog
+ 935: mashed potato
+ 936: cabbage
+ 937: broccoli
+ 938: cauliflower
+ 939: zucchini
+ 940: spaghetti squash
+ 941: acorn squash
+ 942: butternut squash
+ 943: cucumber
+ 944: artichoke
+ 945: bell pepper
+ 946: cardoon
+ 947: mushroom
+ 948: Granny Smith
+ 949: strawberry
+ 950: orange
+ 951: lemon
+ 952: fig
+ 953: pineapple
+ 954: banana
+ 955: jackfruit
+ 956: custard apple
+ 957: pomegranate
+ 958: hay
+ 959: carbonara
+ 960: chocolate syrup
+ 961: dough
+ 962: meatloaf
+ 963: pizza
+ 964: pot pie
+ 965: burrito
+ 966: red wine
+ 967: espresso
+ 968: cup
+ 969: eggnog
+ 970: alp
+ 971: bubble
+ 972: cliff
+ 973: coral reef
+ 974: geyser
+ 975: lakeshore
+ 976: promontory
+ 977: shoal
+ 978: seashore
+ 979: valley
+ 980: volcano
+ 981: baseball player
+ 982: bridegroom
+ 983: scuba diver
+ 984: rapeseed
+ 985: daisy
+ 986: yellow lady's slipper
+ 987: corn
+ 988: acorn
+ 989: rose hip
+ 990: horse chestnut seed
+ 991: coral fungus
+ 992: agaric
+ 993: gyromitra
+ 994: stinkhorn mushroom
+ 995: earth star
+ 996: hen-of-the-woods
+ 997: bolete
+ 998: ear
+ 999: toilet paper
+
+# Imagenet class codes to human-readable names
+map:
+ n01440764: tench
+ n01443537: goldfish
+ n01484850: great_white_shark
+ n01491361: tiger_shark
+ n01494475: hammerhead
+ n01496331: electric_ray
+ n01498041: stingray
+ n01514668: cock
+ n01514859: hen
+ n01518878: ostrich
+ n01530575: brambling
+ n01531178: goldfinch
+ n01532829: house_finch
+ n01534433: junco
+ n01537544: indigo_bunting
+ n01558993: robin
+ n01560419: bulbul
+ n01580077: jay
+ n01582220: magpie
+ n01592084: chickadee
+ n01601694: water_ouzel
+ n01608432: kite
+ n01614925: bald_eagle
+ n01616318: vulture
+ n01622779: great_grey_owl
+ n01629819: European_fire_salamander
+ n01630670: common_newt
+ n01631663: eft
+ n01632458: spotted_salamander
+ n01632777: axolotl
+ n01641577: bullfrog
+ n01644373: tree_frog
+ n01644900: tailed_frog
+ n01664065: loggerhead
+ n01665541: leatherback_turtle
+ n01667114: mud_turtle
+ n01667778: terrapin
+ n01669191: box_turtle
+ n01675722: banded_gecko
+ n01677366: common_iguana
+ n01682714: American_chameleon
+ n01685808: whiptail
+ n01687978: agama
+ n01688243: frilled_lizard
+ n01689811: alligator_lizard
+ n01692333: Gila_monster
+ n01693334: green_lizard
+ n01694178: African_chameleon
+ n01695060: Komodo_dragon
+ n01697457: African_crocodile
+ n01698640: American_alligator
+ n01704323: triceratops
+ n01728572: thunder_snake
+ n01728920: ringneck_snake
+ n01729322: hognose_snake
+ n01729977: green_snake
+ n01734418: king_snake
+ n01735189: garter_snake
+ n01737021: water_snake
+ n01739381: vine_snake
+ n01740131: night_snake
+ n01742172: boa_constrictor
+ n01744401: rock_python
+ n01748264: Indian_cobra
+ n01749939: green_mamba
+ n01751748: sea_snake
+ n01753488: horned_viper
+ n01755581: diamondback
+ n01756291: sidewinder
+ n01768244: trilobite
+ n01770081: harvestman
+ n01770393: scorpion
+ n01773157: black_and_gold_garden_spider
+ n01773549: barn_spider
+ n01773797: garden_spider
+ n01774384: black_widow
+ n01774750: tarantula
+ n01775062: wolf_spider
+ n01776313: tick
+ n01784675: centipede
+ n01795545: black_grouse
+ n01796340: ptarmigan
+ n01797886: ruffed_grouse
+ n01798484: prairie_chicken
+ n01806143: peacock
+ n01806567: quail
+ n01807496: partridge
+ n01817953: African_grey
+ n01818515: macaw
+ n01819313: sulphur-crested_cockatoo
+ n01820546: lorikeet
+ n01824575: coucal
+ n01828970: bee_eater
+ n01829413: hornbill
+ n01833805: hummingbird
+ n01843065: jacamar
+ n01843383: toucan
+ n01847000: drake
+ n01855032: red-breasted_merganser
+ n01855672: goose
+ n01860187: black_swan
+ n01871265: tusker
+ n01872401: echidna
+ n01873310: platypus
+ n01877812: wallaby
+ n01882714: koala
+ n01883070: wombat
+ n01910747: jellyfish
+ n01914609: sea_anemone
+ n01917289: brain_coral
+ n01924916: flatworm
+ n01930112: nematode
+ n01943899: conch
+ n01944390: snail
+ n01945685: slug
+ n01950731: sea_slug
+ n01955084: chiton
+ n01968897: chambered_nautilus
+ n01978287: Dungeness_crab
+ n01978455: rock_crab
+ n01980166: fiddler_crab
+ n01981276: king_crab
+ n01983481: American_lobster
+ n01984695: spiny_lobster
+ n01985128: crayfish
+ n01986214: hermit_crab
+ n01990800: isopod
+ n02002556: white_stork
+ n02002724: black_stork
+ n02006656: spoonbill
+ n02007558: flamingo
+ n02009229: little_blue_heron
+ n02009912: American_egret
+ n02011460: bittern
+ n02012849: crane_(bird)
+ n02013706: limpkin
+ n02017213: European_gallinule
+ n02018207: American_coot
+ n02018795: bustard
+ n02025239: ruddy_turnstone
+ n02027492: red-backed_sandpiper
+ n02028035: redshank
+ n02033041: dowitcher
+ n02037110: oystercatcher
+ n02051845: pelican
+ n02056570: king_penguin
+ n02058221: albatross
+ n02066245: grey_whale
+ n02071294: killer_whale
+ n02074367: dugong
+ n02077923: sea_lion
+ n02085620: Chihuahua
+ n02085782: Japanese_spaniel
+ n02085936: Maltese_dog
+ n02086079: Pekinese
+ n02086240: Shih-Tzu
+ n02086646: Blenheim_spaniel
+ n02086910: papillon
+ n02087046: toy_terrier
+ n02087394: Rhodesian_ridgeback
+ n02088094: Afghan_hound
+ n02088238: basset
+ n02088364: beagle
+ n02088466: bloodhound
+ n02088632: bluetick
+ n02089078: black-and-tan_coonhound
+ n02089867: Walker_hound
+ n02089973: English_foxhound
+ n02090379: redbone
+ n02090622: borzoi
+ n02090721: Irish_wolfhound
+ n02091032: Italian_greyhound
+ n02091134: whippet
+ n02091244: Ibizan_hound
+ n02091467: Norwegian_elkhound
+ n02091635: otterhound
+ n02091831: Saluki
+ n02092002: Scottish_deerhound
+ n02092339: Weimaraner
+ n02093256: Staffordshire_bullterrier
+ n02093428: American_Staffordshire_terrier
+ n02093647: Bedlington_terrier
+ n02093754: Border_terrier
+ n02093859: Kerry_blue_terrier
+ n02093991: Irish_terrier
+ n02094114: Norfolk_terrier
+ n02094258: Norwich_terrier
+ n02094433: Yorkshire_terrier
+ n02095314: wire-haired_fox_terrier
+ n02095570: Lakeland_terrier
+ n02095889: Sealyham_terrier
+ n02096051: Airedale
+ n02096177: cairn
+ n02096294: Australian_terrier
+ n02096437: Dandie_Dinmont
+ n02096585: Boston_bull
+ n02097047: miniature_schnauzer
+ n02097130: giant_schnauzer
+ n02097209: standard_schnauzer
+ n02097298: Scotch_terrier
+ n02097474: Tibetan_terrier
+ n02097658: silky_terrier
+ n02098105: soft-coated_wheaten_terrier
+ n02098286: West_Highland_white_terrier
+ n02098413: Lhasa
+ n02099267: flat-coated_retriever
+ n02099429: curly-coated_retriever
+ n02099601: golden_retriever
+ n02099712: Labrador_retriever
+ n02099849: Chesapeake_Bay_retriever
+ n02100236: German_short-haired_pointer
+ n02100583: vizsla
+ n02100735: English_setter
+ n02100877: Irish_setter
+ n02101006: Gordon_setter
+ n02101388: Brittany_spaniel
+ n02101556: clumber
+ n02102040: English_springer
+ n02102177: Welsh_springer_spaniel
+ n02102318: cocker_spaniel
+ n02102480: Sussex_spaniel
+ n02102973: Irish_water_spaniel
+ n02104029: kuvasz
+ n02104365: schipperke
+ n02105056: groenendael
+ n02105162: malinois
+ n02105251: briard
+ n02105412: kelpie
+ n02105505: komondor
+ n02105641: Old_English_sheepdog
+ n02105855: Shetland_sheepdog
+ n02106030: collie
+ n02106166: Border_collie
+ n02106382: Bouvier_des_Flandres
+ n02106550: Rottweiler
+ n02106662: German_shepherd
+ n02107142: Doberman
+ n02107312: miniature_pinscher
+ n02107574: Greater_Swiss_Mountain_dog
+ n02107683: Bernese_mountain_dog
+ n02107908: Appenzeller
+ n02108000: EntleBucher
+ n02108089: boxer
+ n02108422: bull_mastiff
+ n02108551: Tibetan_mastiff
+ n02108915: French_bulldog
+ n02109047: Great_Dane
+ n02109525: Saint_Bernard
+ n02109961: Eskimo_dog
+ n02110063: malamute
+ n02110185: Siberian_husky
+ n02110341: dalmatian
+ n02110627: affenpinscher
+ n02110806: basenji
+ n02110958: pug
+ n02111129: Leonberg
+ n02111277: Newfoundland
+ n02111500: Great_Pyrenees
+ n02111889: Samoyed
+ n02112018: Pomeranian
+ n02112137: chow
+ n02112350: keeshond
+ n02112706: Brabancon_griffon
+ n02113023: Pembroke
+ n02113186: Cardigan
+ n02113624: toy_poodle
+ n02113712: miniature_poodle
+ n02113799: standard_poodle
+ n02113978: Mexican_hairless
+ n02114367: timber_wolf
+ n02114548: white_wolf
+ n02114712: red_wolf
+ n02114855: coyote
+ n02115641: dingo
+ n02115913: dhole
+ n02116738: African_hunting_dog
+ n02117135: hyena
+ n02119022: red_fox
+ n02119789: kit_fox
+ n02120079: Arctic_fox
+ n02120505: grey_fox
+ n02123045: tabby
+ n02123159: tiger_cat
+ n02123394: Persian_cat
+ n02123597: Siamese_cat
+ n02124075: Egyptian_cat
+ n02125311: cougar
+ n02127052: lynx
+ n02128385: leopard
+ n02128757: snow_leopard
+ n02128925: jaguar
+ n02129165: lion
+ n02129604: tiger
+ n02130308: cheetah
+ n02132136: brown_bear
+ n02133161: American_black_bear
+ n02134084: ice_bear
+ n02134418: sloth_bear
+ n02137549: mongoose
+ n02138441: meerkat
+ n02165105: tiger_beetle
+ n02165456: ladybug
+ n02167151: ground_beetle
+ n02168699: long-horned_beetle
+ n02169497: leaf_beetle
+ n02172182: dung_beetle
+ n02174001: rhinoceros_beetle
+ n02177972: weevil
+ n02190166: fly
+ n02206856: bee
+ n02219486: ant
+ n02226429: grasshopper
+ n02229544: cricket
+ n02231487: walking_stick
+ n02233338: cockroach
+ n02236044: mantis
+ n02256656: cicada
+ n02259212: leafhopper
+ n02264363: lacewing
+ n02268443: dragonfly
+ n02268853: damselfly
+ n02276258: admiral
+ n02277742: ringlet
+ n02279972: monarch
+ n02280649: cabbage_butterfly
+ n02281406: sulphur_butterfly
+ n02281787: lycaenid
+ n02317335: starfish
+ n02319095: sea_urchin
+ n02321529: sea_cucumber
+ n02325366: wood_rabbit
+ n02326432: hare
+ n02328150: Angora
+ n02342885: hamster
+ n02346627: porcupine
+ n02356798: fox_squirrel
+ n02361337: marmot
+ n02363005: beaver
+ n02364673: guinea_pig
+ n02389026: sorrel
+ n02391049: zebra
+ n02395406: hog
+ n02396427: wild_boar
+ n02397096: warthog
+ n02398521: hippopotamus
+ n02403003: ox
+ n02408429: water_buffalo
+ n02410509: bison
+ n02412080: ram
+ n02415577: bighorn
+ n02417914: ibex
+ n02422106: hartebeest
+ n02422699: impala
+ n02423022: gazelle
+ n02437312: Arabian_camel
+ n02437616: llama
+ n02441942: weasel
+ n02442845: mink
+ n02443114: polecat
+ n02443484: black-footed_ferret
+ n02444819: otter
+ n02445715: skunk
+ n02447366: badger
+ n02454379: armadillo
+ n02457408: three-toed_sloth
+ n02480495: orangutan
+ n02480855: gorilla
+ n02481823: chimpanzee
+ n02483362: gibbon
+ n02483708: siamang
+ n02484975: guenon
+ n02486261: patas
+ n02486410: baboon
+ n02487347: macaque
+ n02488291: langur
+ n02488702: colobus
+ n02489166: proboscis_monkey
+ n02490219: marmoset
+ n02492035: capuchin
+ n02492660: howler_monkey
+ n02493509: titi
+ n02493793: spider_monkey
+ n02494079: squirrel_monkey
+ n02497673: Madagascar_cat
+ n02500267: indri
+ n02504013: Indian_elephant
+ n02504458: African_elephant
+ n02509815: lesser_panda
+ n02510455: giant_panda
+ n02514041: barracouta
+ n02526121: eel
+ n02536864: coho
+ n02606052: rock_beauty
+ n02607072: anemone_fish
+ n02640242: sturgeon
+ n02641379: gar
+ n02643566: lionfish
+ n02655020: puffer
+ n02666196: abacus
+ n02667093: abaya
+ n02669723: academic_gown
+ n02672831: accordion
+ n02676566: acoustic_guitar
+ n02687172: aircraft_carrier
+ n02690373: airliner
+ n02692877: airship
+ n02699494: altar
+ n02701002: ambulance
+ n02704792: amphibian
+ n02708093: analog_clock
+ n02727426: apiary
+ n02730930: apron
+ n02747177: ashcan
+ n02749479: assault_rifle
+ n02769748: backpack
+ n02776631: bakery
+ n02777292: balance_beam
+ n02782093: balloon
+ n02783161: ballpoint
+ n02786058: Band_Aid
+ n02787622: banjo
+ n02788148: bannister
+ n02790996: barbell
+ n02791124: barber_chair
+ n02791270: barbershop
+ n02793495: barn
+ n02794156: barometer
+ n02795169: barrel
+ n02797295: barrow
+ n02799071: baseball
+ n02802426: basketball
+ n02804414: bassinet
+ n02804610: bassoon
+ n02807133: bathing_cap
+ n02808304: bath_towel
+ n02808440: bathtub
+ n02814533: beach_wagon
+ n02814860: beacon
+ n02815834: beaker
+ n02817516: bearskin
+ n02823428: beer_bottle
+ n02823750: beer_glass
+ n02825657: bell_cote
+ n02834397: bib
+ n02835271: bicycle-built-for-two
+ n02837789: bikini
+ n02840245: binder
+ n02841315: binoculars
+ n02843684: birdhouse
+ n02859443: boathouse
+ n02860847: bobsled
+ n02865351: bolo_tie
+ n02869837: bonnet
+ n02870880: bookcase
+ n02871525: bookshop
+ n02877765: bottlecap
+ n02879718: bow
+ n02883205: bow_tie
+ n02892201: brass
+ n02892767: brassiere
+ n02894605: breakwater
+ n02895154: breastplate
+ n02906734: broom
+ n02909870: bucket
+ n02910353: buckle
+ n02916936: bulletproof_vest
+ n02917067: bullet_train
+ n02927161: butcher_shop
+ n02930766: cab
+ n02939185: caldron
+ n02948072: candle
+ n02950826: cannon
+ n02951358: canoe
+ n02951585: can_opener
+ n02963159: cardigan
+ n02965783: car_mirror
+ n02966193: carousel
+ n02966687: carpenter's_kit
+ n02971356: carton
+ n02974003: car_wheel
+ n02977058: cash_machine
+ n02978881: cassette
+ n02979186: cassette_player
+ n02980441: castle
+ n02981792: catamaran
+ n02988304: CD_player
+ n02992211: cello
+ n02992529: cellular_telephone
+ n02999410: chain
+ n03000134: chainlink_fence
+ n03000247: chain_mail
+ n03000684: chain_saw
+ n03014705: chest
+ n03016953: chiffonier
+ n03017168: chime
+ n03018349: china_cabinet
+ n03026506: Christmas_stocking
+ n03028079: church
+ n03032252: cinema
+ n03041632: cleaver
+ n03042490: cliff_dwelling
+ n03045698: cloak
+ n03047690: clog
+ n03062245: cocktail_shaker
+ n03063599: coffee_mug
+ n03063689: coffeepot
+ n03065424: coil
+ n03075370: combination_lock
+ n03085013: computer_keyboard
+ n03089624: confectionery
+ n03095699: container_ship
+ n03100240: convertible
+ n03109150: corkscrew
+ n03110669: cornet
+ n03124043: cowboy_boot
+ n03124170: cowboy_hat
+ n03125729: cradle
+ n03126707: crane_(machine)
+ n03127747: crash_helmet
+ n03127925: crate
+ n03131574: crib
+ n03133878: Crock_Pot
+ n03134739: croquet_ball
+ n03141823: crutch
+ n03146219: cuirass
+ n03160309: dam
+ n03179701: desk
+ n03180011: desktop_computer
+ n03187595: dial_telephone
+ n03188531: diaper
+ n03196217: digital_clock
+ n03197337: digital_watch
+ n03201208: dining_table
+ n03207743: dishrag
+ n03207941: dishwasher
+ n03208938: disk_brake
+ n03216828: dock
+ n03218198: dogsled
+ n03220513: dome
+ n03223299: doormat
+ n03240683: drilling_platform
+ n03249569: drum
+ n03250847: drumstick
+ n03255030: dumbbell
+ n03259280: Dutch_oven
+ n03271574: electric_fan
+ n03272010: electric_guitar
+ n03272562: electric_locomotive
+ n03290653: entertainment_center
+ n03291819: envelope
+ n03297495: espresso_maker
+ n03314780: face_powder
+ n03325584: feather_boa
+ n03337140: file
+ n03344393: fireboat
+ n03345487: fire_engine
+ n03347037: fire_screen
+ n03355925: flagpole
+ n03372029: flute
+ n03376595: folding_chair
+ n03379051: football_helmet
+ n03384352: forklift
+ n03388043: fountain
+ n03388183: fountain_pen
+ n03388549: four-poster
+ n03393912: freight_car
+ n03394916: French_horn
+ n03400231: frying_pan
+ n03404251: fur_coat
+ n03417042: garbage_truck
+ n03424325: gasmask
+ n03425413: gas_pump
+ n03443371: goblet
+ n03444034: go-kart
+ n03445777: golf_ball
+ n03445924: golfcart
+ n03447447: gondola
+ n03447721: gong
+ n03450230: gown
+ n03452741: grand_piano
+ n03457902: greenhouse
+ n03459775: grille
+ n03461385: grocery_store
+ n03467068: guillotine
+ n03476684: hair_slide
+ n03476991: hair_spray
+ n03478589: half_track
+ n03481172: hammer
+ n03482405: hamper
+ n03483316: hand_blower
+ n03485407: hand-held_computer
+ n03485794: handkerchief
+ n03492542: hard_disc
+ n03494278: harmonica
+ n03495258: harp
+ n03496892: harvester
+ n03498962: hatchet
+ n03527444: holster
+ n03529860: home_theater
+ n03530642: honeycomb
+ n03532672: hook
+ n03534580: hoopskirt
+ n03535780: horizontal_bar
+ n03538406: horse_cart
+ n03544143: hourglass
+ n03584254: iPod
+ n03584829: iron
+ n03590841: jack-o'-lantern
+ n03594734: jean
+ n03594945: jeep
+ n03595614: jersey
+ n03598930: jigsaw_puzzle
+ n03599486: jinrikisha
+ n03602883: joystick
+ n03617480: kimono
+ n03623198: knee_pad
+ n03627232: knot
+ n03630383: lab_coat
+ n03633091: ladle
+ n03637318: lampshade
+ n03642806: laptop
+ n03649909: lawn_mower
+ n03657121: lens_cap
+ n03658185: letter_opener
+ n03661043: library
+ n03662601: lifeboat
+ n03666591: lighter
+ n03670208: limousine
+ n03673027: liner
+ n03676483: lipstick
+ n03680355: Loafer
+ n03690938: lotion
+ n03691459: loudspeaker
+ n03692522: loupe
+ n03697007: lumbermill
+ n03706229: magnetic_compass
+ n03709823: mailbag
+ n03710193: mailbox
+ n03710637: maillot_(tights)
+ n03710721: maillot_(tank_suit)
+ n03717622: manhole_cover
+ n03720891: maraca
+ n03721384: marimba
+ n03724870: mask
+ n03729826: matchstick
+ n03733131: maypole
+ n03733281: maze
+ n03733805: measuring_cup
+ n03742115: medicine_chest
+ n03743016: megalith
+ n03759954: microphone
+ n03761084: microwave
+ n03763968: military_uniform
+ n03764736: milk_can
+ n03769881: minibus
+ n03770439: miniskirt
+ n03770679: minivan
+ n03773504: missile
+ n03775071: mitten
+ n03775546: mixing_bowl
+ n03776460: mobile_home
+ n03777568: Model_T
+ n03777754: modem
+ n03781244: monastery
+ n03782006: monitor
+ n03785016: moped
+ n03786901: mortar
+ n03787032: mortarboard
+ n03788195: mosque
+ n03788365: mosquito_net
+ n03791053: motor_scooter
+ n03792782: mountain_bike
+ n03792972: mountain_tent
+ n03793489: mouse
+ n03794056: mousetrap
+ n03796401: moving_van
+ n03803284: muzzle
+ n03804744: nail
+ n03814639: neck_brace
+ n03814906: necklace
+ n03825788: nipple
+ n03832673: notebook
+ n03837869: obelisk
+ n03838899: oboe
+ n03840681: ocarina
+ n03841143: odometer
+ n03843555: oil_filter
+ n03854065: organ
+ n03857828: oscilloscope
+ n03866082: overskirt
+ n03868242: oxcart
+ n03868863: oxygen_mask
+ n03871628: packet
+ n03873416: paddle
+ n03874293: paddlewheel
+ n03874599: padlock
+ n03876231: paintbrush
+ n03877472: pajama
+ n03877845: palace
+ n03884397: panpipe
+ n03887697: paper_towel
+ n03888257: parachute
+ n03888605: parallel_bars
+ n03891251: park_bench
+ n03891332: parking_meter
+ n03895866: passenger_car
+ n03899768: patio
+ n03902125: pay-phone
+ n03903868: pedestal
+ n03908618: pencil_box
+ n03908714: pencil_sharpener
+ n03916031: perfume
+ n03920288: Petri_dish
+ n03924679: photocopier
+ n03929660: pick
+ n03929855: pickelhaube
+ n03930313: picket_fence
+ n03930630: pickup
+ n03933933: pier
+ n03935335: piggy_bank
+ n03937543: pill_bottle
+ n03938244: pillow
+ n03942813: ping-pong_ball
+ n03944341: pinwheel
+ n03947888: pirate
+ n03950228: pitcher
+ n03954731: plane
+ n03956157: planetarium
+ n03958227: plastic_bag
+ n03961711: plate_rack
+ n03967562: plow
+ n03970156: plunger
+ n03976467: Polaroid_camera
+ n03976657: pole
+ n03977966: police_van
+ n03980874: poncho
+ n03982430: pool_table
+ n03983396: pop_bottle
+ n03991062: pot
+ n03992509: potter's_wheel
+ n03995372: power_drill
+ n03998194: prayer_rug
+ n04004767: printer
+ n04005630: prison
+ n04008634: projectile
+ n04009552: projector
+ n04019541: puck
+ n04023962: punching_bag
+ n04026417: purse
+ n04033901: quill
+ n04033995: quilt
+ n04037443: racer
+ n04039381: racket
+ n04040759: radiator
+ n04041544: radio
+ n04044716: radio_telescope
+ n04049303: rain_barrel
+ n04065272: recreational_vehicle
+ n04067472: reel
+ n04069434: reflex_camera
+ n04070727: refrigerator
+ n04074963: remote_control
+ n04081281: restaurant
+ n04086273: revolver
+ n04090263: rifle
+ n04099969: rocking_chair
+ n04111531: rotisserie
+ n04116512: rubber_eraser
+ n04118538: rugby_ball
+ n04118776: rule
+ n04120489: running_shoe
+ n04125021: safe
+ n04127249: safety_pin
+ n04131690: saltshaker
+ n04133789: sandal
+ n04136333: sarong
+ n04141076: sax
+ n04141327: scabbard
+ n04141975: scale
+ n04146614: school_bus
+ n04147183: schooner
+ n04149813: scoreboard
+ n04152593: screen
+ n04153751: screw
+ n04154565: screwdriver
+ n04162706: seat_belt
+ n04179913: sewing_machine
+ n04192698: shield
+ n04200800: shoe_shop
+ n04201297: shoji
+ n04204238: shopping_basket
+ n04204347: shopping_cart
+ n04208210: shovel
+ n04209133: shower_cap
+ n04209239: shower_curtain
+ n04228054: ski
+ n04229816: ski_mask
+ n04235860: sleeping_bag
+ n04238763: slide_rule
+ n04239074: sliding_door
+ n04243546: slot
+ n04251144: snorkel
+ n04252077: snowmobile
+ n04252225: snowplow
+ n04254120: soap_dispenser
+ n04254680: soccer_ball
+ n04254777: sock
+ n04258138: solar_dish
+ n04259630: sombrero
+ n04263257: soup_bowl
+ n04264628: space_bar
+ n04265275: space_heater
+ n04266014: space_shuttle
+ n04270147: spatula
+ n04273569: speedboat
+ n04275548: spider_web
+ n04277352: spindle
+ n04285008: sports_car
+ n04286575: spotlight
+ n04296562: stage
+ n04310018: steam_locomotive
+ n04311004: steel_arch_bridge
+ n04311174: steel_drum
+ n04317175: stethoscope
+ n04325704: stole
+ n04326547: stone_wall
+ n04328186: stopwatch
+ n04330267: stove
+ n04332243: strainer
+ n04335435: streetcar
+ n04336792: stretcher
+ n04344873: studio_couch
+ n04346328: stupa
+ n04347754: submarine
+ n04350905: suit
+ n04355338: sundial
+ n04355933: sunglass
+ n04356056: sunglasses
+ n04357314: sunscreen
+ n04366367: suspension_bridge
+ n04367480: swab
+ n04370456: sweatshirt
+ n04371430: swimming_trunks
+ n04371774: swing
+ n04372370: switch
+ n04376876: syringe
+ n04380533: table_lamp
+ n04389033: tank
+ n04392985: tape_player
+ n04398044: teapot
+ n04399382: teddy
+ n04404412: television
+ n04409515: tennis_ball
+ n04417672: thatch
+ n04418357: theater_curtain
+ n04423845: thimble
+ n04428191: thresher
+ n04429376: throne
+ n04435653: tile_roof
+ n04442312: toaster
+ n04443257: tobacco_shop
+ n04447861: toilet_seat
+ n04456115: torch
+ n04458633: totem_pole
+ n04461696: tow_truck
+ n04462240: toyshop
+ n04465501: tractor
+ n04467665: trailer_truck
+ n04476259: tray
+ n04479046: trench_coat
+ n04482393: tricycle
+ n04483307: trimaran
+ n04485082: tripod
+ n04486054: triumphal_arch
+ n04487081: trolleybus
+ n04487394: trombone
+ n04493381: tub
+ n04501370: turnstile
+ n04505470: typewriter_keyboard
+ n04507155: umbrella
+ n04509417: unicycle
+ n04515003: upright
+ n04517823: vacuum
+ n04522168: vase
+ n04523525: vault
+ n04525038: velvet
+ n04525305: vending_machine
+ n04532106: vestment
+ n04532670: viaduct
+ n04536866: violin
+ n04540053: volleyball
+ n04542943: waffle_iron
+ n04548280: wall_clock
+ n04548362: wallet
+ n04550184: wardrobe
+ n04552348: warplane
+ n04553703: washbasin
+ n04554684: washer
+ n04557648: water_bottle
+ n04560804: water_jug
+ n04562935: water_tower
+ n04579145: whiskey_jug
+ n04579432: whistle
+ n04584207: wig
+ n04589890: window_screen
+ n04590129: window_shade
+ n04591157: Windsor_tie
+ n04591713: wine_bottle
+ n04592741: wing
+ n04596742: wok
+ n04597913: wooden_spoon
+ n04599235: wool
+ n04604644: worm_fence
+ n04606251: wreck
+ n04612504: yawl
+ n04613696: yurt
+ n06359193: web_site
+ n06596364: comic_book
+ n06785654: crossword_puzzle
+ n06794110: street_sign
+ n06874185: traffic_light
+ n07248320: book_jacket
+ n07565083: menu
+ n07579787: plate
+ n07583066: guacamole
+ n07584110: consomme
+ n07590611: hot_pot
+ n07613480: trifle
+ n07614500: ice_cream
+ n07615774: ice_lolly
+ n07684084: French_loaf
+ n07693725: bagel
+ n07695742: pretzel
+ n07697313: cheeseburger
+ n07697537: hotdog
+ n07711569: mashed_potato
+ n07714571: head_cabbage
+ n07714990: broccoli
+ n07715103: cauliflower
+ n07716358: zucchini
+ n07716906: spaghetti_squash
+ n07717410: acorn_squash
+ n07717556: butternut_squash
+ n07718472: cucumber
+ n07718747: artichoke
+ n07720875: bell_pepper
+ n07730033: cardoon
+ n07734744: mushroom
+ n07742313: Granny_Smith
+ n07745940: strawberry
+ n07747607: orange
+ n07749582: lemon
+ n07753113: fig
+ n07753275: pineapple
+ n07753592: banana
+ n07754684: jackfruit
+ n07760859: custard_apple
+ n07768694: pomegranate
+ n07802026: hay
+ n07831146: carbonara
+ n07836838: chocolate_sauce
+ n07860988: dough
+ n07871810: meat_loaf
+ n07873807: pizza
+ n07875152: potpie
+ n07880968: burrito
+ n07892512: red_wine
+ n07920052: espresso
+ n07930864: cup
+ n07932039: eggnog
+ n09193705: alp
+ n09229709: bubble
+ n09246464: cliff
+ n09256479: coral_reef
+ n09288635: geyser
+ n09332890: lakeside
+ n09399592: promontory
+ n09421951: sandbar
+ n09428293: seashore
+ n09468604: valley
+ n09472597: volcano
+ n09835506: ballplayer
+ n10148035: groom
+ n10565667: scuba_diver
+ n11879895: rapeseed
+ n11939491: daisy
+ n12057211: yellow_lady's_slipper
+ n12144580: corn
+ n12267677: acorn
+ n12620546: hip
+ n12768682: buckeye
+ n12985857: coral_fungus
+ n12998815: agaric
+ n13037406: gyromitra
+ n13040303: stinkhorn
+ n13044778: earthstar
+ n13052670: hen-of-the-woods
+ n13054560: bolete
+ n13133613: ear
+ n15075141: toilet_tissue
+
+# Download script/URL (optional)
+download: yolo/data/scripts/get_imagenet.sh
diff --git a/ultralytics/cfg/datasets/Objects365.yaml b/ultralytics/cfg/datasets/Objects365.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..89921364a5252d9d4eb54029953d648021c0484a
--- /dev/null
+++ b/ultralytics/cfg/datasets/Objects365.yaml
@@ -0,0 +1,443 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Objects365 dataset https://www.objects365.org/ by Megvii
+# Documentation: https://docs.ultralytics.com/datasets/detect/objects365/
+# Example usage: yolo train data=Objects365.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── Objects365 ← downloads here (712 GB = 367G data + 345G zips)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/Objects365 # dataset root dir
+train: images/train # train images (relative to 'path') 1742289 images
+val: images/val # val images (relative to 'path') 80000 images
+test: # test images (optional)
+
+# Classes
+names:
+ 0: Person
+ 1: Sneakers
+ 2: Chair
+ 3: Other Shoes
+ 4: Hat
+ 5: Car
+ 6: Lamp
+ 7: Glasses
+ 8: Bottle
+ 9: Desk
+ 10: Cup
+ 11: Street Lights
+ 12: Cabinet/shelf
+ 13: Handbag/Satchel
+ 14: Bracelet
+ 15: Plate
+ 16: Picture/Frame
+ 17: Helmet
+ 18: Book
+ 19: Gloves
+ 20: Storage box
+ 21: Boat
+ 22: Leather Shoes
+ 23: Flower
+ 24: Bench
+ 25: Potted Plant
+ 26: Bowl/Basin
+ 27: Flag
+ 28: Pillow
+ 29: Boots
+ 30: Vase
+ 31: Microphone
+ 32: Necklace
+ 33: Ring
+ 34: SUV
+ 35: Wine Glass
+ 36: Belt
+ 37: Monitor/TV
+ 38: Backpack
+ 39: Umbrella
+ 40: Traffic Light
+ 41: Speaker
+ 42: Watch
+ 43: Tie
+ 44: Trash bin Can
+ 45: Slippers
+ 46: Bicycle
+ 47: Stool
+ 48: Barrel/bucket
+ 49: Van
+ 50: Couch
+ 51: Sandals
+ 52: Basket
+ 53: Drum
+ 54: Pen/Pencil
+ 55: Bus
+ 56: Wild Bird
+ 57: High Heels
+ 58: Motorcycle
+ 59: Guitar
+ 60: Carpet
+ 61: Cell Phone
+ 62: Bread
+ 63: Camera
+ 64: Canned
+ 65: Truck
+ 66: Traffic cone
+ 67: Cymbal
+ 68: Lifesaver
+ 69: Towel
+ 70: Stuffed Toy
+ 71: Candle
+ 72: Sailboat
+ 73: Laptop
+ 74: Awning
+ 75: Bed
+ 76: Faucet
+ 77: Tent
+ 78: Horse
+ 79: Mirror
+ 80: Power outlet
+ 81: Sink
+ 82: Apple
+ 83: Air Conditioner
+ 84: Knife
+ 85: Hockey Stick
+ 86: Paddle
+ 87: Pickup Truck
+ 88: Fork
+ 89: Traffic Sign
+ 90: Balloon
+ 91: Tripod
+ 92: Dog
+ 93: Spoon
+ 94: Clock
+ 95: Pot
+ 96: Cow
+ 97: Cake
+ 98: Dining Table
+ 99: Sheep
+ 100: Hanger
+ 101: Blackboard/Whiteboard
+ 102: Napkin
+ 103: Other Fish
+ 104: Orange/Tangerine
+ 105: Toiletry
+ 106: Keyboard
+ 107: Tomato
+ 108: Lantern
+ 109: Machinery Vehicle
+ 110: Fan
+ 111: Green Vegetables
+ 112: Banana
+ 113: Baseball Glove
+ 114: Airplane
+ 115: Mouse
+ 116: Train
+ 117: Pumpkin
+ 118: Soccer
+ 119: Skiboard
+ 120: Luggage
+ 121: Nightstand
+ 122: Tea pot
+ 123: Telephone
+ 124: Trolley
+ 125: Head Phone
+ 126: Sports Car
+ 127: Stop Sign
+ 128: Dessert
+ 129: Scooter
+ 130: Stroller
+ 131: Crane
+ 132: Remote
+ 133: Refrigerator
+ 134: Oven
+ 135: Lemon
+ 136: Duck
+ 137: Baseball Bat
+ 138: Surveillance Camera
+ 139: Cat
+ 140: Jug
+ 141: Broccoli
+ 142: Piano
+ 143: Pizza
+ 144: Elephant
+ 145: Skateboard
+ 146: Surfboard
+ 147: Gun
+ 148: Skating and Skiing shoes
+ 149: Gas stove
+ 150: Donut
+ 151: Bow Tie
+ 152: Carrot
+ 153: Toilet
+ 154: Kite
+ 155: Strawberry
+ 156: Other Balls
+ 157: Shovel
+ 158: Pepper
+ 159: Computer Box
+ 160: Toilet Paper
+ 161: Cleaning Products
+ 162: Chopsticks
+ 163: Microwave
+ 164: Pigeon
+ 165: Baseball
+ 166: Cutting/chopping Board
+ 167: Coffee Table
+ 168: Side Table
+ 169: Scissors
+ 170: Marker
+ 171: Pie
+ 172: Ladder
+ 173: Snowboard
+ 174: Cookies
+ 175: Radiator
+ 176: Fire Hydrant
+ 177: Basketball
+ 178: Zebra
+ 179: Grape
+ 180: Giraffe
+ 181: Potato
+ 182: Sausage
+ 183: Tricycle
+ 184: Violin
+ 185: Egg
+ 186: Fire Extinguisher
+ 187: Candy
+ 188: Fire Truck
+ 189: Billiards
+ 190: Converter
+ 191: Bathtub
+ 192: Wheelchair
+ 193: Golf Club
+ 194: Briefcase
+ 195: Cucumber
+ 196: Cigar/Cigarette
+ 197: Paint Brush
+ 198: Pear
+ 199: Heavy Truck
+ 200: Hamburger
+ 201: Extractor
+ 202: Extension Cord
+ 203: Tong
+ 204: Tennis Racket
+ 205: Folder
+ 206: American Football
+ 207: earphone
+ 208: Mask
+ 209: Kettle
+ 210: Tennis
+ 211: Ship
+ 212: Swing
+ 213: Coffee Machine
+ 214: Slide
+ 215: Carriage
+ 216: Onion
+ 217: Green beans
+ 218: Projector
+ 219: Frisbee
+ 220: Washing Machine/Drying Machine
+ 221: Chicken
+ 222: Printer
+ 223: Watermelon
+ 224: Saxophone
+ 225: Tissue
+ 226: Toothbrush
+ 227: Ice cream
+ 228: Hot-air balloon
+ 229: Cello
+ 230: French Fries
+ 231: Scale
+ 232: Trophy
+ 233: Cabbage
+ 234: Hot dog
+ 235: Blender
+ 236: Peach
+ 237: Rice
+ 238: Wallet/Purse
+ 239: Volleyball
+ 240: Deer
+ 241: Goose
+ 242: Tape
+ 243: Tablet
+ 244: Cosmetics
+ 245: Trumpet
+ 246: Pineapple
+ 247: Golf Ball
+ 248: Ambulance
+ 249: Parking meter
+ 250: Mango
+ 251: Key
+ 252: Hurdle
+ 253: Fishing Rod
+ 254: Medal
+ 255: Flute
+ 256: Brush
+ 257: Penguin
+ 258: Megaphone
+ 259: Corn
+ 260: Lettuce
+ 261: Garlic
+ 262: Swan
+ 263: Helicopter
+ 264: Green Onion
+ 265: Sandwich
+ 266: Nuts
+ 267: Speed Limit Sign
+ 268: Induction Cooker
+ 269: Broom
+ 270: Trombone
+ 271: Plum
+ 272: Rickshaw
+ 273: Goldfish
+ 274: Kiwi fruit
+ 275: Router/modem
+ 276: Poker Card
+ 277: Toaster
+ 278: Shrimp
+ 279: Sushi
+ 280: Cheese
+ 281: Notepaper
+ 282: Cherry
+ 283: Pliers
+ 284: CD
+ 285: Pasta
+ 286: Hammer
+ 287: Cue
+ 288: Avocado
+ 289: Hami melon
+ 290: Flask
+ 291: Mushroom
+ 292: Screwdriver
+ 293: Soap
+ 294: Recorder
+ 295: Bear
+ 296: Eggplant
+ 297: Board Eraser
+ 298: Coconut
+ 299: Tape Measure/Ruler
+ 300: Pig
+ 301: Showerhead
+ 302: Globe
+ 303: Chips
+ 304: Steak
+ 305: Crosswalk Sign
+ 306: Stapler
+ 307: Camel
+ 308: Formula 1
+ 309: Pomegranate
+ 310: Dishwasher
+ 311: Crab
+ 312: Hoverboard
+ 313: Meatball
+ 314: Rice Cooker
+ 315: Tuba
+ 316: Calculator
+ 317: Papaya
+ 318: Antelope
+ 319: Parrot
+ 320: Seal
+ 321: Butterfly
+ 322: Dumbbell
+ 323: Donkey
+ 324: Lion
+ 325: Urinal
+ 326: Dolphin
+ 327: Electric Drill
+ 328: Hair Dryer
+ 329: Egg tart
+ 330: Jellyfish
+ 331: Treadmill
+ 332: Lighter
+ 333: Grapefruit
+ 334: Game board
+ 335: Mop
+ 336: Radish
+ 337: Baozi
+ 338: Target
+ 339: French
+ 340: Spring Rolls
+ 341: Monkey
+ 342: Rabbit
+ 343: Pencil Case
+ 344: Yak
+ 345: Red Cabbage
+ 346: Binoculars
+ 347: Asparagus
+ 348: Barbell
+ 349: Scallop
+ 350: Noddles
+ 351: Comb
+ 352: Dumpling
+ 353: Oyster
+ 354: Table Tennis paddle
+ 355: Cosmetics Brush/Eyeliner Pencil
+ 356: Chainsaw
+ 357: Eraser
+ 358: Lobster
+ 359: Durian
+ 360: Okra
+ 361: Lipstick
+ 362: Cosmetics Mirror
+ 363: Curling
+ 364: Table Tennis
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ from tqdm import tqdm
+
+ from ultralytics.utils.checks import check_requirements
+ from ultralytics.utils.downloads import download
+ from ultralytics.utils.ops import xyxy2xywhn
+
+ import numpy as np
+ from pathlib import Path
+
+ check_requirements(('pycocotools>=2.0',))
+ from pycocotools.coco import COCO
+
+ # Make Directories
+ dir = Path(yaml['path']) # dataset root dir
+ for p in 'images', 'labels':
+ (dir / p).mkdir(parents=True, exist_ok=True)
+ for q in 'train', 'val':
+ (dir / p / q).mkdir(parents=True, exist_ok=True)
+
+ # Train, Val Splits
+ for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
+ print(f"Processing {split} in {patches} patches ...")
+ images, labels = dir / 'images' / split, dir / 'labels' / split
+
+ # Download
+ url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
+ if split == 'train':
+ download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir) # annotations json
+ download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, threads=8)
+ elif split == 'val':
+ download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir) # annotations json
+ download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, threads=8)
+ download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, threads=8)
+
+ # Move
+ for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
+ f.rename(images / f.name) # move to /images/{split}
+
+ # Labels
+ coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
+ names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
+ for cid, cat in enumerate(names):
+ catIds = coco.getCatIds(catNms=[cat])
+ imgIds = coco.getImgIds(catIds=catIds)
+ for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
+ width, height = im["width"], im["height"]
+ path = Path(im["file_name"]) # image filename
+ try:
+ with open(labels / path.with_suffix('.txt').name, 'a') as file:
+ annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None)
+ for a in coco.loadAnns(annIds):
+ x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner)
+ xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4)
+ x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped
+ file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
+ except Exception as e:
+ print(e)
diff --git a/ultralytics/cfg/datasets/SKU-110K.yaml b/ultralytics/cfg/datasets/SKU-110K.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a2c94ced1bc48f6afec204f77e1c28d4fb8884ce
--- /dev/null
+++ b/ultralytics/cfg/datasets/SKU-110K.yaml
@@ -0,0 +1,58 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
+# Documentation: https://docs.ultralytics.com/datasets/detect/sku-110k/
+# Example usage: yolo train data=SKU-110K.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── SKU-110K ← downloads here (13.6 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/SKU-110K # dataset root dir
+train: train.txt # train images (relative to 'path') 8219 images
+val: val.txt # val images (relative to 'path') 588 images
+test: test.txt # test images (optional) 2936 images
+
+# Classes
+names:
+ 0: object
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ import shutil
+ from pathlib import Path
+
+ import numpy as np
+ import pandas as pd
+ from tqdm import tqdm
+
+ from ultralytics.utils.downloads import download
+ from ultralytics.utils.ops import xyxy2xywh
+
+ # Download
+ dir = Path(yaml['path']) # dataset root dir
+ parent = Path(dir.parent) # download dir
+ urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
+ download(urls, dir=parent)
+
+ # Rename directories
+ if dir.exists():
+ shutil.rmtree(dir)
+ (parent / 'SKU110K_fixed').rename(dir) # rename dir
+ (dir / 'labels').mkdir(parents=True, exist_ok=True) # create labels dir
+
+ # Convert labels
+ names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height' # column names
+ for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv':
+ x = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations
+ images, unique_images = x[:, 0], np.unique(x[:, 0])
+ with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f:
+ f.writelines(f'./images/{s}\n' for s in unique_images)
+ for im in tqdm(unique_images, desc=f'Converting {dir / d}'):
+ cls = 0 # single-class dataset
+ with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f:
+ for r in x[images == im]:
+ w, h = r[6], r[7] # image width, height
+ xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance
+ f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n") # write label
diff --git a/ultralytics/cfg/datasets/VOC.yaml b/ultralytics/cfg/datasets/VOC.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2eb06ffdeb4e9bc9b8ea9abe698e481abe52d367
--- /dev/null
+++ b/ultralytics/cfg/datasets/VOC.yaml
@@ -0,0 +1,100 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
+# Documentation: # Documentation: https://docs.ultralytics.com/datasets/detect/voc/
+# Example usage: yolo train data=VOC.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── VOC ← downloads here (2.8 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/VOC
+train: # train images (relative to 'path') 16551 images
+ - images/train2012
+ - images/train2007
+ - images/val2012
+ - images/val2007
+val: # val images (relative to 'path') 4952 images
+ - images/test2007
+test: # test images (optional)
+ - images/test2007
+
+# Classes
+names:
+ 0: aeroplane
+ 1: bicycle
+ 2: bird
+ 3: boat
+ 4: bottle
+ 5: bus
+ 6: car
+ 7: cat
+ 8: chair
+ 9: cow
+ 10: diningtable
+ 11: dog
+ 12: horse
+ 13: motorbike
+ 14: person
+ 15: pottedplant
+ 16: sheep
+ 17: sofa
+ 18: train
+ 19: tvmonitor
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ import xml.etree.ElementTree as ET
+
+ from tqdm import tqdm
+ from ultralytics.utils.downloads import download
+ from pathlib import Path
+
+ def convert_label(path, lb_path, year, image_id):
+ def convert_box(size, box):
+ dw, dh = 1. / size[0], 1. / size[1]
+ x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
+ return x * dw, y * dh, w * dw, h * dh
+
+ in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')
+ out_file = open(lb_path, 'w')
+ tree = ET.parse(in_file)
+ root = tree.getroot()
+ size = root.find('size')
+ w = int(size.find('width').text)
+ h = int(size.find('height').text)
+
+ names = list(yaml['names'].values()) # names list
+ for obj in root.iter('object'):
+ cls = obj.find('name').text
+ if cls in names and int(obj.find('difficult').text) != 1:
+ xmlbox = obj.find('bndbox')
+ bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
+ cls_id = names.index(cls) # class id
+ out_file.write(" ".join(str(a) for a in (cls_id, *bb)) + '\n')
+
+
+ # Download
+ dir = Path(yaml['path']) # dataset root dir
+ url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
+ urls = [f'{url}VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
+ f'{url}VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
+ f'{url}VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
+ download(urls, dir=dir / 'images', curl=True, threads=3, exist_ok=True) # download and unzip over existing paths (required)
+
+ # Convert
+ path = dir / 'images/VOCdevkit'
+ for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
+ imgs_path = dir / 'images' / f'{image_set}{year}'
+ lbs_path = dir / 'labels' / f'{image_set}{year}'
+ imgs_path.mkdir(exist_ok=True, parents=True)
+ lbs_path.mkdir(exist_ok=True, parents=True)
+
+ with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:
+ image_ids = f.read().strip().split()
+ for id in tqdm(image_ids, desc=f'{image_set}{year}'):
+ f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path
+ lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path
+ f.rename(imgs_path / f.name) # move image
+ convert_label(path, lb_path, year, id) # convert labels to YOLO format
diff --git a/ultralytics/cfg/datasets/VisDrone.yaml b/ultralytics/cfg/datasets/VisDrone.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9fc7b45e435c21ce7d33d59bfe5d69d0446f9201
--- /dev/null
+++ b/ultralytics/cfg/datasets/VisDrone.yaml
@@ -0,0 +1,73 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
+# Documentation: https://docs.ultralytics.com/datasets/detect/visdrone/
+# Example usage: yolo train data=VisDrone.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── VisDrone ← downloads here (2.3 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/VisDrone # dataset root dir
+train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images
+val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images
+test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images
+
+# Classes
+names:
+ 0: pedestrian
+ 1: people
+ 2: bicycle
+ 3: car
+ 4: van
+ 5: truck
+ 6: tricycle
+ 7: awning-tricycle
+ 8: bus
+ 9: motor
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ import os
+ from pathlib import Path
+
+ from ultralytics.utils.downloads import download
+
+ def visdrone2yolo(dir):
+ from PIL import Image
+ from tqdm import tqdm
+
+ def convert_box(size, box):
+ # Convert VisDrone box to YOLO xywh box
+ dw = 1. / size[0]
+ dh = 1. / size[1]
+ return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
+
+ (dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory
+ pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
+ for f in pbar:
+ img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
+ lines = []
+ with open(f, 'r') as file: # read annotation.txt
+ for row in [x.split(',') for x in file.read().strip().splitlines()]:
+ if row[4] == '0': # VisDrone 'ignored regions' class 0
+ continue
+ cls = int(row[5]) - 1
+ box = convert_box(img_size, tuple(map(int, row[:4])))
+ lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
+ with open(str(f).replace(f'{os.sep}annotations{os.sep}', f'{os.sep}labels{os.sep}'), 'w') as fl:
+ fl.writelines(lines) # write label.txt
+
+
+ # Download
+ dir = Path(yaml['path']) # dataset root dir
+ urls = ['https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-train.zip',
+ 'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-val.zip',
+ 'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-dev.zip',
+ 'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-challenge.zip']
+ download(urls, dir=dir, curl=True, threads=4)
+
+ # Convert
+ for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
+ visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels
diff --git a/ultralytics/cfg/datasets/african-wildlife.yaml b/ultralytics/cfg/datasets/african-wildlife.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b825f8f068b54a47e4f32cd105ca225f3f9f1f8a
--- /dev/null
+++ b/ultralytics/cfg/datasets/african-wildlife.yaml
@@ -0,0 +1,25 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# African-wildlife dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/african-wildlife/
+# Example usage: yolo train data=african-wildlife.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── african-wildlife ← downloads here (100 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/african-wildlife # dataset root dir
+train: train/images # train images (relative to 'path') 1052 images
+val: valid/images # val images (relative to 'path') 225 images
+test: test/images # test images (relative to 'path') 227 images
+
+# Classes
+names:
+ 0: buffalo
+ 1: elephant
+ 2: rhino
+ 3: zebra
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/african-wildlife.zip
diff --git a/ultralytics/cfg/datasets/brain-tumor.yaml b/ultralytics/cfg/datasets/brain-tumor.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7a448e84afc76832914cde3a2f0d6208b8c78e29
--- /dev/null
+++ b/ultralytics/cfg/datasets/brain-tumor.yaml
@@ -0,0 +1,23 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Brain-tumor dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/brain-tumor/
+# Example usage: yolo train data=brain-tumor.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── brain-tumor ← downloads here (4.05 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/brain-tumor # dataset root dir
+train: train/images # train images (relative to 'path') 893 images
+val: valid/images # val images (relative to 'path') 223 images
+test: # test images (relative to 'path')
+
+# Classes
+names:
+ 0: negative
+ 1: positive
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/brain-tumor.zip
diff --git a/ultralytics/cfg/datasets/carparts-seg.yaml b/ultralytics/cfg/datasets/carparts-seg.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9f15f9b06625e2a2575d24da262d317a9394e71a
--- /dev/null
+++ b/ultralytics/cfg/datasets/carparts-seg.yaml
@@ -0,0 +1,44 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Carparts-seg dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/segment/carparts-seg/
+# Example usage: yolo train data=carparts-seg.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── carparts-seg ← downloads here (132 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/carparts-seg # dataset root dir
+train: train/images # train images (relative to 'path') 3516 images
+val: valid/images # val images (relative to 'path') 276 images
+test: test/images # test images (relative to 'path') 401 images
+
+# Classes
+names:
+ 0: back_bumper
+ 1: back_door
+ 2: back_glass
+ 3: back_left_door
+ 4: back_left_light
+ 5: back_light
+ 6: back_right_door
+ 7: back_right_light
+ 8: front_bumper
+ 9: front_door
+ 10: front_glass
+ 11: front_left_door
+ 12: front_left_light
+ 13: front_light
+ 14: front_right_door
+ 15: front_right_light
+ 16: hood
+ 17: left_mirror
+ 18: object
+ 19: right_mirror
+ 20: tailgate
+ 21: trunk
+ 22: wheel
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/carparts-seg.zip
diff --git a/ultralytics/cfg/datasets/coco-pose.yaml b/ultralytics/cfg/datasets/coco-pose.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..353dcd721b0ed5d9a437775a121ee63cf6d916e7
--- /dev/null
+++ b/ultralytics/cfg/datasets/coco-pose.yaml
@@ -0,0 +1,39 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO 2017 Keypoints dataset https://cocodataset.org by Microsoft
+# Documentation: https://docs.ultralytics.com/datasets/pose/coco/
+# Example usage: yolo train data=coco-pose.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── coco-pose ← downloads here (20.1 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco-pose # dataset root dir
+train: train2017.txt # train images (relative to 'path') 56599 images
+val: val2017.txt # val images (relative to 'path') 2346 images
+test: test-dev2017.txt # 20288 of 40670 images, submit to https://codalab.lisn.upsaclay.fr/competitions/7403
+
+# Keypoints
+kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
+
+# Classes
+names:
+ 0: person
+
+# Download script/URL (optional)
+download: |
+ from ultralytics.utils.downloads import download
+ from pathlib import Path
+
+ # Download labels
+ dir = Path(yaml['path']) # dataset root dir
+ url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
+ urls = [url + 'coco2017labels-pose.zip'] # labels
+ download(urls, dir=dir.parent)
+ # Download data
+ urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images
+ 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images
+ 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional)
+ download(urls, dir=dir / 'images', threads=3)
diff --git a/ultralytics/cfg/datasets/coco.yaml b/ultralytics/cfg/datasets/coco.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..255b2af1a10999a2271bc43bff650077e6ab020a
--- /dev/null
+++ b/ultralytics/cfg/datasets/coco.yaml
@@ -0,0 +1,115 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO 2017 dataset https://cocodataset.org by Microsoft
+# Documentation: https://docs.ultralytics.com/datasets/detect/coco/
+# Example usage: yolo train data=coco.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── coco ← downloads here (20.1 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ..datasets/coco # dataset root dir
+train: train2017.txt # train images (relative to 'path') 118287 images
+val: val2017.txt # val images (relative to 'path') 5000 images
+test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
+
+# Classes
+names:
+ 0: person
+ 1: bicycle
+ 2: car
+ 3: motorcycle
+ 4: airplane
+ 5: bus
+ 6: train
+ 7: truck
+ 8: boat
+ 9: traffic light
+ 10: fire hydrant
+ 11: stop sign
+ 12: parking meter
+ 13: bench
+ 14: bird
+ 15: cat
+ 16: dog
+ 17: horse
+ 18: sheep
+ 19: cow
+ 20: elephant
+ 21: bear
+ 22: zebra
+ 23: giraffe
+ 24: backpack
+ 25: umbrella
+ 26: handbag
+ 27: tie
+ 28: suitcase
+ 29: frisbee
+ 30: skis
+ 31: snowboard
+ 32: sports ball
+ 33: kite
+ 34: baseball bat
+ 35: baseball glove
+ 36: skateboard
+ 37: surfboard
+ 38: tennis racket
+ 39: bottle
+ 40: wine glass
+ 41: cup
+ 42: fork
+ 43: knife
+ 44: spoon
+ 45: bowl
+ 46: banana
+ 47: apple
+ 48: sandwich
+ 49: orange
+ 50: broccoli
+ 51: carrot
+ 52: hot dog
+ 53: pizza
+ 54: donut
+ 55: cake
+ 56: chair
+ 57: couch
+ 58: potted plant
+ 59: bed
+ 60: dining table
+ 61: toilet
+ 62: tv
+ 63: laptop
+ 64: mouse
+ 65: remote
+ 66: keyboard
+ 67: cell phone
+ 68: microwave
+ 69: oven
+ 70: toaster
+ 71: sink
+ 72: refrigerator
+ 73: book
+ 74: clock
+ 75: vase
+ 76: scissors
+ 77: teddy bear
+ 78: hair drier
+ 79: toothbrush
+
+# Download script/URL (optional)
+download: |
+ from ultralytics.utils.downloads import download
+ from pathlib import Path
+
+ # Download labels
+ segments = True # segment or box labels
+ dir = Path(yaml['path']) # dataset root dir
+ url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
+ urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels
+ download(urls, dir=dir.parent)
+ # Download data
+ urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images
+ 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images
+ 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional)
+ download(urls, dir=dir / 'images', threads=3)
diff --git a/ultralytics/cfg/datasets/coco128-seg.yaml b/ultralytics/cfg/datasets/coco128-seg.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b023c676300db3ca9909839b5cdf7ac709ba5949
--- /dev/null
+++ b/ultralytics/cfg/datasets/coco128-seg.yaml
@@ -0,0 +1,101 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO128-seg dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/segment/coco/
+# Example usage: yolo train data=coco128.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── coco128-seg ← downloads here (7 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco128-seg # dataset root dir
+train: images/train2017 # train images (relative to 'path') 128 images
+val: images/train2017 # val images (relative to 'path') 128 images
+test: # test images (optional)
+
+# Classes
+names:
+ 0: person
+ 1: bicycle
+ 2: car
+ 3: motorcycle
+ 4: airplane
+ 5: bus
+ 6: train
+ 7: truck
+ 8: boat
+ 9: traffic light
+ 10: fire hydrant
+ 11: stop sign
+ 12: parking meter
+ 13: bench
+ 14: bird
+ 15: cat
+ 16: dog
+ 17: horse
+ 18: sheep
+ 19: cow
+ 20: elephant
+ 21: bear
+ 22: zebra
+ 23: giraffe
+ 24: backpack
+ 25: umbrella
+ 26: handbag
+ 27: tie
+ 28: suitcase
+ 29: frisbee
+ 30: skis
+ 31: snowboard
+ 32: sports ball
+ 33: kite
+ 34: baseball bat
+ 35: baseball glove
+ 36: skateboard
+ 37: surfboard
+ 38: tennis racket
+ 39: bottle
+ 40: wine glass
+ 41: cup
+ 42: fork
+ 43: knife
+ 44: spoon
+ 45: bowl
+ 46: banana
+ 47: apple
+ 48: sandwich
+ 49: orange
+ 50: broccoli
+ 51: carrot
+ 52: hot dog
+ 53: pizza
+ 54: donut
+ 55: cake
+ 56: chair
+ 57: couch
+ 58: potted plant
+ 59: bed
+ 60: dining table
+ 61: toilet
+ 62: tv
+ 63: laptop
+ 64: mouse
+ 65: remote
+ 66: keyboard
+ 67: cell phone
+ 68: microwave
+ 69: oven
+ 70: toaster
+ 71: sink
+ 72: refrigerator
+ 73: book
+ 74: clock
+ 75: vase
+ 76: scissors
+ 77: teddy bear
+ 78: hair drier
+ 79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128-seg.zip
diff --git a/ultralytics/cfg/datasets/coco128.yaml b/ultralytics/cfg/datasets/coco128.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..12ff0511bcd0df62be6f05762743543e1eb21524
--- /dev/null
+++ b/ultralytics/cfg/datasets/coco128.yaml
@@ -0,0 +1,101 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO128 dataset https://www.kaggle.com/datasets/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/coco/
+# Example usage: yolo train data=coco128.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── coco128 ← downloads here (7 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco128 # dataset root dir
+train: images/train2017 # train images (relative to 'path') 128 images
+val: images/train2017 # val images (relative to 'path') 128 images
+test: # test images (optional)
+
+# Classes
+names:
+ 0: person
+ 1: bicycle
+ 2: car
+ 3: motorcycle
+ 4: airplane
+ 5: bus
+ 6: train
+ 7: truck
+ 8: boat
+ 9: traffic light
+ 10: fire hydrant
+ 11: stop sign
+ 12: parking meter
+ 13: bench
+ 14: bird
+ 15: cat
+ 16: dog
+ 17: horse
+ 18: sheep
+ 19: cow
+ 20: elephant
+ 21: bear
+ 22: zebra
+ 23: giraffe
+ 24: backpack
+ 25: umbrella
+ 26: handbag
+ 27: tie
+ 28: suitcase
+ 29: frisbee
+ 30: skis
+ 31: snowboard
+ 32: sports ball
+ 33: kite
+ 34: baseball bat
+ 35: baseball glove
+ 36: skateboard
+ 37: surfboard
+ 38: tennis racket
+ 39: bottle
+ 40: wine glass
+ 41: cup
+ 42: fork
+ 43: knife
+ 44: spoon
+ 45: bowl
+ 46: banana
+ 47: apple
+ 48: sandwich
+ 49: orange
+ 50: broccoli
+ 51: carrot
+ 52: hot dog
+ 53: pizza
+ 54: donut
+ 55: cake
+ 56: chair
+ 57: couch
+ 58: potted plant
+ 59: bed
+ 60: dining table
+ 61: toilet
+ 62: tv
+ 63: laptop
+ 64: mouse
+ 65: remote
+ 66: keyboard
+ 67: cell phone
+ 68: microwave
+ 69: oven
+ 70: toaster
+ 71: sink
+ 72: refrigerator
+ 73: book
+ 74: clock
+ 75: vase
+ 76: scissors
+ 77: teddy bear
+ 78: hair drier
+ 79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128.zip
diff --git a/ultralytics/cfg/datasets/coco8-pose.yaml b/ultralytics/cfg/datasets/coco8-pose.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3e8af1e344804571664f5be7de288ba5ff7c3822
--- /dev/null
+++ b/ultralytics/cfg/datasets/coco8-pose.yaml
@@ -0,0 +1,26 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO8-pose dataset (first 8 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/pose/coco8-pose/
+# Example usage: yolo train data=coco8-pose.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── coco8-pose ← downloads here (1 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco8-pose # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+test: # test images (optional)
+
+# Keypoints
+kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
+
+# Classes
+names:
+ 0: person
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8-pose.zip
diff --git a/ultralytics/cfg/datasets/coco8-seg.yaml b/ultralytics/cfg/datasets/coco8-seg.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1ea6b31004cbf5fe33914387532d7e46b1967aa8
--- /dev/null
+++ b/ultralytics/cfg/datasets/coco8-seg.yaml
@@ -0,0 +1,101 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO8-seg dataset (first 8 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/segment/coco8-seg/
+# Example usage: yolo train data=coco8-seg.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── coco8-seg ← downloads here (1 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco8-seg # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+test: # test images (optional)
+
+# Classes
+names:
+ 0: person
+ 1: bicycle
+ 2: car
+ 3: motorcycle
+ 4: airplane
+ 5: bus
+ 6: train
+ 7: truck
+ 8: boat
+ 9: traffic light
+ 10: fire hydrant
+ 11: stop sign
+ 12: parking meter
+ 13: bench
+ 14: bird
+ 15: cat
+ 16: dog
+ 17: horse
+ 18: sheep
+ 19: cow
+ 20: elephant
+ 21: bear
+ 22: zebra
+ 23: giraffe
+ 24: backpack
+ 25: umbrella
+ 26: handbag
+ 27: tie
+ 28: suitcase
+ 29: frisbee
+ 30: skis
+ 31: snowboard
+ 32: sports ball
+ 33: kite
+ 34: baseball bat
+ 35: baseball glove
+ 36: skateboard
+ 37: surfboard
+ 38: tennis racket
+ 39: bottle
+ 40: wine glass
+ 41: cup
+ 42: fork
+ 43: knife
+ 44: spoon
+ 45: bowl
+ 46: banana
+ 47: apple
+ 48: sandwich
+ 49: orange
+ 50: broccoli
+ 51: carrot
+ 52: hot dog
+ 53: pizza
+ 54: donut
+ 55: cake
+ 56: chair
+ 57: couch
+ 58: potted plant
+ 59: bed
+ 60: dining table
+ 61: toilet
+ 62: tv
+ 63: laptop
+ 64: mouse
+ 65: remote
+ 66: keyboard
+ 67: cell phone
+ 68: microwave
+ 69: oven
+ 70: toaster
+ 71: sink
+ 72: refrigerator
+ 73: book
+ 74: clock
+ 75: vase
+ 76: scissors
+ 77: teddy bear
+ 78: hair drier
+ 79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8-seg.zip
diff --git a/ultralytics/cfg/datasets/coco8.yaml b/ultralytics/cfg/datasets/coco8.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8200738b46d0c5414afc6b2c68027bcfe40b739c
--- /dev/null
+++ b/ultralytics/cfg/datasets/coco8.yaml
@@ -0,0 +1,101 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO8 dataset (first 8 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/coco8/
+# Example usage: yolo train data=coco8.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── coco8 ← downloads here (1 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco8 # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+test: # test images (optional)
+
+# Classes
+names:
+ 0: person
+ 1: bicycle
+ 2: car
+ 3: motorcycle
+ 4: airplane
+ 5: bus
+ 6: train
+ 7: truck
+ 8: boat
+ 9: traffic light
+ 10: fire hydrant
+ 11: stop sign
+ 12: parking meter
+ 13: bench
+ 14: bird
+ 15: cat
+ 16: dog
+ 17: horse
+ 18: sheep
+ 19: cow
+ 20: elephant
+ 21: bear
+ 22: zebra
+ 23: giraffe
+ 24: backpack
+ 25: umbrella
+ 26: handbag
+ 27: tie
+ 28: suitcase
+ 29: frisbee
+ 30: skis
+ 31: snowboard
+ 32: sports ball
+ 33: kite
+ 34: baseball bat
+ 35: baseball glove
+ 36: skateboard
+ 37: surfboard
+ 38: tennis racket
+ 39: bottle
+ 40: wine glass
+ 41: cup
+ 42: fork
+ 43: knife
+ 44: spoon
+ 45: bowl
+ 46: banana
+ 47: apple
+ 48: sandwich
+ 49: orange
+ 50: broccoli
+ 51: carrot
+ 52: hot dog
+ 53: pizza
+ 54: donut
+ 55: cake
+ 56: chair
+ 57: couch
+ 58: potted plant
+ 59: bed
+ 60: dining table
+ 61: toilet
+ 62: tv
+ 63: laptop
+ 64: mouse
+ 65: remote
+ 66: keyboard
+ 67: cell phone
+ 68: microwave
+ 69: oven
+ 70: toaster
+ 71: sink
+ 72: refrigerator
+ 73: book
+ 74: clock
+ 75: vase
+ 76: scissors
+ 77: teddy bear
+ 78: hair drier
+ 79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco8.zip
diff --git a/ultralytics/cfg/datasets/crack-seg.yaml b/ultralytics/cfg/datasets/crack-seg.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..11bdd5f575fe7d8d9046636cb595ce707e8601a7
--- /dev/null
+++ b/ultralytics/cfg/datasets/crack-seg.yaml
@@ -0,0 +1,22 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Crack-seg dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/segment/crack-seg/
+# Example usage: yolo train data=crack-seg.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── crack-seg ← downloads here (91.2 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/crack-seg # dataset root dir
+train: train/images # train images (relative to 'path') 3717 images
+val: valid/images # val images (relative to 'path') 112 images
+test: test/images # test images (relative to 'path') 200 images
+
+# Classes
+names:
+ 0: crack
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/crack-seg.zip
diff --git a/ultralytics/cfg/datasets/dog-pose.yaml b/ultralytics/cfg/datasets/dog-pose.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..447e542ce6c124533e25c7fa2a5caed88570d4ec
--- /dev/null
+++ b/ultralytics/cfg/datasets/dog-pose.yaml
@@ -0,0 +1,24 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Dogs dataset http://vision.stanford.edu/aditya86/ImageNetDogs/ by Stanford
+# Documentation: https://docs.ultralytics.com/datasets/pose/dog-pose/
+# Example usage: yolo train data=dog-pose.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── dog-pose ← downloads here (337 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/dog-pose # dataset root dir
+train: train # train images (relative to 'path') 6773 images
+val: val # val images (relative to 'path') 1703 images
+
+# Keypoints
+kpt_shape: [24, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+
+# Classes
+names:
+ 0: dog
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/dog-pose.zip
diff --git a/ultralytics/cfg/datasets/dota8.yaml b/ultralytics/cfg/datasets/dota8.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..486d9e2effbd0e2161ff982d2df2aea16b6d9fa0
--- /dev/null
+++ b/ultralytics/cfg/datasets/dota8.yaml
@@ -0,0 +1,35 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# DOTA8 dataset 8 images from split DOTAv1 dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/obb/dota8/
+# Example usage: yolo train model=yolov8n-obb.pt data=dota8.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── dota8 ← downloads here (1MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/dota8 # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+
+# Classes for DOTA 1.0
+names:
+ 0: plane
+ 1: ship
+ 2: storage tank
+ 3: baseball diamond
+ 4: tennis court
+ 5: basketball court
+ 6: ground track field
+ 7: harbor
+ 8: bridge
+ 9: large vehicle
+ 10: small vehicle
+ 11: helicopter
+ 12: roundabout
+ 13: soccer ball field
+ 14: swimming pool
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/dota8.zip
diff --git a/ultralytics/cfg/datasets/hand-keypoints.yaml b/ultralytics/cfg/datasets/hand-keypoints.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6d2f765c789c828efa2b993249c238e466985eb3
--- /dev/null
+++ b/ultralytics/cfg/datasets/hand-keypoints.yaml
@@ -0,0 +1,26 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Hand Keypoints dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/pose/hand-keypoints/
+# Example usage: yolo train data=hand-keypoints.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── hand-keypoints ← downloads here (369 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/hand-keypoints # dataset root dir
+train: train # train images (relative to 'path') 18776 images
+val: val # val images (relative to 'path') 7992 images
+
+# Keypoints
+kpt_shape: [21, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+flip_idx:
+ [0, 1, 2, 4, 3, 10, 11, 12, 13, 14, 5, 6, 7, 8, 9, 15, 16, 17, 18, 19, 20]
+
+# Classes
+names:
+ 0: hand
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/hand-keypoints.zip
diff --git a/ultralytics/cfg/datasets/lvis.yaml b/ultralytics/cfg/datasets/lvis.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..22030ac90797d95686282fcaba2b2c12b4a0fc00
--- /dev/null
+++ b/ultralytics/cfg/datasets/lvis.yaml
@@ -0,0 +1,1236 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# LVIS dataset http://www.lvisdataset.org by Facebook AI Research.
+# Documentation: https://docs.ultralytics.com/datasets/detect/lvis/
+# Example usage: yolo train data=lvis.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── lvis ← downloads here (20.1 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/lvis # dataset root dir
+train: train.txt # train images (relative to 'path') 100170 images
+val: val.txt # val images (relative to 'path') 19809 images
+minival: minival.txt # minival images (relative to 'path') 5000 images
+
+names:
+ 0: aerosol can/spray can
+ 1: air conditioner
+ 2: airplane/aeroplane
+ 3: alarm clock
+ 4: alcohol/alcoholic beverage
+ 5: alligator/gator
+ 6: almond
+ 7: ambulance
+ 8: amplifier
+ 9: anklet/ankle bracelet
+ 10: antenna/aerial/transmitting aerial
+ 11: apple
+ 12: applesauce
+ 13: apricot
+ 14: apron
+ 15: aquarium/fish tank
+ 16: arctic/arctic type of shoe/galosh/golosh/rubber/rubber type of shoe/gumshoe
+ 17: armband
+ 18: armchair
+ 19: armoire
+ 20: armor/armour
+ 21: artichoke
+ 22: trash can/garbage can/wastebin/dustbin/trash barrel/trash bin
+ 23: ashtray
+ 24: asparagus
+ 25: atomizer/atomiser/spray/sprayer/nebulizer/nebuliser
+ 26: avocado
+ 27: award/accolade
+ 28: awning
+ 29: ax/axe
+ 30: baboon
+ 31: baby buggy/baby carriage/perambulator/pram/stroller
+ 32: basketball backboard
+ 33: backpack/knapsack/packsack/rucksack/haversack
+ 34: handbag/purse/pocketbook
+ 35: suitcase/baggage/luggage
+ 36: bagel/beigel
+ 37: bagpipe
+ 38: baguet/baguette
+ 39: bait/lure
+ 40: ball
+ 41: ballet skirt/tutu
+ 42: balloon
+ 43: bamboo
+ 44: banana
+ 45: Band Aid
+ 46: bandage
+ 47: bandanna/bandana
+ 48: banjo
+ 49: banner/streamer
+ 50: barbell
+ 51: barge
+ 52: barrel/cask
+ 53: barrette
+ 54: barrow/garden cart/lawn cart/wheelbarrow
+ 55: baseball base
+ 56: baseball
+ 57: baseball bat
+ 58: baseball cap/jockey cap/golf cap
+ 59: baseball glove/baseball mitt
+ 60: basket/handbasket
+ 61: basketball
+ 62: bass horn/sousaphone/tuba
+ 63: bat/bat animal
+ 64: bath mat
+ 65: bath towel
+ 66: bathrobe
+ 67: bathtub/bathing tub
+ 68: batter/batter food
+ 69: battery
+ 70: beachball
+ 71: bead
+ 72: bean curd/tofu
+ 73: beanbag
+ 74: beanie/beany
+ 75: bear
+ 76: bed
+ 77: bedpan
+ 78: bedspread/bedcover/bed covering/counterpane/spread
+ 79: cow
+ 80: beef/beef food/boeuf/boeuf food
+ 81: beeper/pager
+ 82: beer bottle
+ 83: beer can
+ 84: beetle
+ 85: bell
+ 86: bell pepper/capsicum
+ 87: belt
+ 88: belt buckle
+ 89: bench
+ 90: beret
+ 91: bib
+ 92: Bible
+ 93: bicycle/bike/bike bicycle
+ 94: visor/vizor
+ 95: billboard
+ 96: binder/ring-binder
+ 97: binoculars/field glasses/opera glasses
+ 98: bird
+ 99: birdfeeder
+ 100: birdbath
+ 101: birdcage
+ 102: birdhouse
+ 103: birthday cake
+ 104: birthday card
+ 105: pirate flag
+ 106: black sheep
+ 107: blackberry
+ 108: blackboard/chalkboard
+ 109: blanket
+ 110: blazer/sport jacket/sport coat/sports jacket/sports coat
+ 111: blender/liquidizer/liquidiser
+ 112: blimp
+ 113: blinker/flasher
+ 114: blouse
+ 115: blueberry
+ 116: gameboard
+ 117: boat/ship/ship boat
+ 118: bob/bobber/bobfloat
+ 119: bobbin/spool/reel
+ 120: bobby pin/hairgrip
+ 121: boiled egg/coddled egg
+ 122: bolo tie/bolo/bola tie/bola
+ 123: deadbolt
+ 124: bolt
+ 125: bonnet
+ 126: book
+ 127: bookcase
+ 128: booklet/brochure/leaflet/pamphlet
+ 129: bookmark/bookmarker
+ 130: boom microphone/microphone boom
+ 131: boot
+ 132: bottle
+ 133: bottle opener
+ 134: bouquet
+ 135: bow/bow weapon
+ 136: bow/bow decorative ribbons
+ 137: bow-tie/bowtie
+ 138: bowl
+ 139: pipe bowl
+ 140: bowler hat/bowler/derby hat/derby/plug hat
+ 141: bowling ball
+ 142: box
+ 143: boxing glove
+ 144: suspenders
+ 145: bracelet/bangle
+ 146: brass plaque
+ 147: brassiere/bra/bandeau
+ 148: bread-bin/breadbox
+ 149: bread
+ 150: breechcloth/breechclout/loincloth
+ 151: bridal gown/wedding gown/wedding dress
+ 152: briefcase
+ 153: broccoli
+ 154: broach
+ 155: broom
+ 156: brownie
+ 157: brussels sprouts
+ 158: bubble gum
+ 159: bucket/pail
+ 160: horse buggy
+ 161: horned cow
+ 162: bulldog
+ 163: bulldozer/dozer
+ 164: bullet train
+ 165: bulletin board/notice board
+ 166: bulletproof vest
+ 167: bullhorn/megaphone
+ 168: bun/roll
+ 169: bunk bed
+ 170: buoy
+ 171: burrito
+ 172: bus/bus vehicle/autobus/charabanc/double-decker/motorbus/motorcoach
+ 173: business card
+ 174: butter
+ 175: butterfly
+ 176: button
+ 177: cab/cab taxi/taxi/taxicab
+ 178: cabana
+ 179: cabin car/caboose
+ 180: cabinet
+ 181: locker/storage locker
+ 182: cake
+ 183: calculator
+ 184: calendar
+ 185: calf
+ 186: camcorder
+ 187: camel
+ 188: camera
+ 189: camera lens
+ 190: camper/camper vehicle/camping bus/motor home
+ 191: can/tin can
+ 192: can opener/tin opener
+ 193: candle/candlestick
+ 194: candle holder
+ 195: candy bar
+ 196: candy cane
+ 197: walking cane
+ 198: canister/canister
+ 199: canoe
+ 200: cantaloup/cantaloupe
+ 201: canteen
+ 202: cap/cap headwear
+ 203: bottle cap/cap/cap container lid
+ 204: cape
+ 205: cappuccino/coffee cappuccino
+ 206: car/car automobile/auto/auto automobile/automobile
+ 207: railcar/railcar part of a train/railway car/railway car part of a train/railroad car/railroad car part of a train
+ 208: elevator car
+ 209: car battery/automobile battery
+ 210: identity card
+ 211: card
+ 212: cardigan
+ 213: cargo ship/cargo vessel
+ 214: carnation
+ 215: horse carriage
+ 216: carrot
+ 217: tote bag
+ 218: cart
+ 219: carton
+ 220: cash register/register/register for cash transactions
+ 221: casserole
+ 222: cassette
+ 223: cast/plaster cast/plaster bandage
+ 224: cat
+ 225: cauliflower
+ 226: cayenne/cayenne spice/cayenne pepper/cayenne pepper spice/red pepper/red pepper spice
+ 227: CD player
+ 228: celery
+ 229: cellular telephone/cellular phone/cellphone/mobile phone/smart phone
+ 230: chain mail/ring mail/chain armor/chain armour/ring armor/ring armour
+ 231: chair
+ 232: chaise longue/chaise/daybed
+ 233: chalice
+ 234: chandelier
+ 235: chap
+ 236: checkbook/chequebook
+ 237: checkerboard
+ 238: cherry
+ 239: chessboard
+ 240: chicken/chicken animal
+ 241: chickpea/garbanzo
+ 242: chili/chili vegetable/chili pepper/chili pepper vegetable/chilli/chilli vegetable/chilly/chilly vegetable/chile/chile vegetable
+ 243: chime/gong
+ 244: chinaware
+ 245: crisp/crisp potato chip/potato chip
+ 246: poker chip
+ 247: chocolate bar
+ 248: chocolate cake
+ 249: chocolate milk
+ 250: chocolate mousse
+ 251: choker/collar/neckband
+ 252: chopping board/cutting board/chopping block
+ 253: chopstick
+ 254: Christmas tree
+ 255: slide
+ 256: cider/cyder
+ 257: cigar box
+ 258: cigarette
+ 259: cigarette case/cigarette pack
+ 260: cistern/water tank
+ 261: clarinet
+ 262: clasp
+ 263: cleansing agent/cleanser/cleaner
+ 264: cleat/cleat for securing rope
+ 265: clementine
+ 266: clip
+ 267: clipboard
+ 268: clippers/clippers for plants
+ 269: cloak
+ 270: clock/timepiece/timekeeper
+ 271: clock tower
+ 272: clothes hamper/laundry basket/clothes basket
+ 273: clothespin/clothes peg
+ 274: clutch bag
+ 275: coaster
+ 276: coat
+ 277: coat hanger/clothes hanger/dress hanger
+ 278: coatrack/hatrack
+ 279: cock/rooster
+ 280: cockroach
+ 281: cocoa/cocoa beverage/hot chocolate/hot chocolate beverage/drinking chocolate
+ 282: coconut/cocoanut
+ 283: coffee maker/coffee machine
+ 284: coffee table/cocktail table
+ 285: coffeepot
+ 286: coil
+ 287: coin
+ 288: colander/cullender
+ 289: coleslaw/slaw
+ 290: coloring material/colouring material
+ 291: combination lock
+ 292: pacifier/teething ring
+ 293: comic book
+ 294: compass
+ 295: computer keyboard/keyboard/keyboard computer
+ 296: condiment
+ 297: cone/traffic cone
+ 298: control/controller
+ 299: convertible/convertible automobile
+ 300: sofa bed
+ 301: cooker
+ 302: cookie/cooky/biscuit/biscuit cookie
+ 303: cooking utensil
+ 304: cooler/cooler for food/ice chest
+ 305: cork/cork bottle plug/bottle cork
+ 306: corkboard
+ 307: corkscrew/bottle screw
+ 308: edible corn/corn/maize
+ 309: cornbread
+ 310: cornet/horn/trumpet
+ 311: cornice/valance/valance board/pelmet
+ 312: cornmeal
+ 313: corset/girdle
+ 314: costume
+ 315: cougar/puma/catamount/mountain lion/panther
+ 316: coverall
+ 317: cowbell
+ 318: cowboy hat/ten-gallon hat
+ 319: crab/crab animal
+ 320: crabmeat
+ 321: cracker
+ 322: crape/crepe/French pancake
+ 323: crate
+ 324: crayon/wax crayon
+ 325: cream pitcher
+ 326: crescent roll/croissant
+ 327: crib/cot
+ 328: crock pot/earthenware jar
+ 329: crossbar
+ 330: crouton
+ 331: crow
+ 332: crowbar/wrecking bar/pry bar
+ 333: crown
+ 334: crucifix
+ 335: cruise ship/cruise liner
+ 336: police cruiser/patrol car/police car/squad car
+ 337: crumb
+ 338: crutch
+ 339: cub/cub animal
+ 340: cube/square block
+ 341: cucumber/cuke
+ 342: cufflink
+ 343: cup
+ 344: trophy cup
+ 345: cupboard/closet
+ 346: cupcake
+ 347: hair curler/hair roller/hair crimper
+ 348: curling iron
+ 349: curtain/drapery
+ 350: cushion
+ 351: cylinder
+ 352: cymbal
+ 353: dagger
+ 354: dalmatian
+ 355: dartboard
+ 356: date/date fruit
+ 357: deck chair/beach chair
+ 358: deer/cervid
+ 359: dental floss/floss
+ 360: desk
+ 361: detergent
+ 362: diaper
+ 363: diary/journal
+ 364: die/dice
+ 365: dinghy/dory/rowboat
+ 366: dining table
+ 367: tux/tuxedo
+ 368: dish
+ 369: dish antenna
+ 370: dishrag/dishcloth
+ 371: dishtowel/tea towel
+ 372: dishwasher/dishwashing machine
+ 373: dishwasher detergent/dishwashing detergent/dishwashing liquid/dishsoap
+ 374: dispenser
+ 375: diving board
+ 376: Dixie cup/paper cup
+ 377: dog
+ 378: dog collar
+ 379: doll
+ 380: dollar/dollar bill/one dollar bill
+ 381: dollhouse/doll's house
+ 382: dolphin
+ 383: domestic ass/donkey
+ 384: doorknob/doorhandle
+ 385: doormat/welcome mat
+ 386: doughnut/donut
+ 387: dove
+ 388: dragonfly
+ 389: drawer
+ 390: underdrawers/boxers/boxershorts
+ 391: dress/frock
+ 392: dress hat/high hat/opera hat/silk hat/top hat
+ 393: dress suit
+ 394: dresser
+ 395: drill
+ 396: drone
+ 397: dropper/eye dropper
+ 398: drum/drum musical instrument
+ 399: drumstick
+ 400: duck
+ 401: duckling
+ 402: duct tape
+ 403: duffel bag/duffle bag/duffel/duffle
+ 404: dumbbell
+ 405: dumpster
+ 406: dustpan
+ 407: eagle
+ 408: earphone/earpiece/headphone
+ 409: earplug
+ 410: earring
+ 411: easel
+ 412: eclair
+ 413: eel
+ 414: egg/eggs
+ 415: egg roll/spring roll
+ 416: egg yolk/yolk/yolk egg
+ 417: eggbeater/eggwhisk
+ 418: eggplant/aubergine
+ 419: electric chair
+ 420: refrigerator
+ 421: elephant
+ 422: elk/moose
+ 423: envelope
+ 424: eraser
+ 425: escargot
+ 426: eyepatch
+ 427: falcon
+ 428: fan
+ 429: faucet/spigot/tap
+ 430: fedora
+ 431: ferret
+ 432: Ferris wheel
+ 433: ferry/ferryboat
+ 434: fig/fig fruit
+ 435: fighter jet/fighter aircraft/attack aircraft
+ 436: figurine
+ 437: file cabinet/filing cabinet
+ 438: file/file tool
+ 439: fire alarm/smoke alarm
+ 440: fire engine/fire truck
+ 441: fire extinguisher/extinguisher
+ 442: fire hose
+ 443: fireplace
+ 444: fireplug/fire hydrant/hydrant
+ 445: first-aid kit
+ 446: fish
+ 447: fish/fish food
+ 448: fishbowl/goldfish bowl
+ 449: fishing rod/fishing pole
+ 450: flag
+ 451: flagpole/flagstaff
+ 452: flamingo
+ 453: flannel
+ 454: flap
+ 455: flash/flashbulb
+ 456: flashlight/torch
+ 457: fleece
+ 458: flip-flop/flip-flop sandal
+ 459: flipper/flipper footwear/fin/fin footwear
+ 460: flower arrangement/floral arrangement
+ 461: flute glass/champagne flute
+ 462: foal
+ 463: folding chair
+ 464: food processor
+ 465: football/football American
+ 466: football helmet
+ 467: footstool/footrest
+ 468: fork
+ 469: forklift
+ 470: freight car
+ 471: French toast
+ 472: freshener/air freshener
+ 473: frisbee
+ 474: frog/toad/toad frog
+ 475: fruit juice
+ 476: frying pan/frypan/skillet
+ 477: fudge
+ 478: funnel
+ 479: futon
+ 480: gag/muzzle
+ 481: garbage
+ 482: garbage truck
+ 483: garden hose
+ 484: gargle/mouthwash
+ 485: gargoyle
+ 486: garlic/ail
+ 487: gasmask/respirator/gas helmet
+ 488: gazelle
+ 489: gelatin/jelly
+ 490: gemstone
+ 491: generator
+ 492: giant panda/panda/panda bear
+ 493: gift wrap
+ 494: ginger/gingerroot
+ 495: giraffe
+ 496: cincture/sash/waistband/waistcloth
+ 497: glass/glass drink container/drinking glass
+ 498: globe
+ 499: glove
+ 500: goat
+ 501: goggles
+ 502: goldfish
+ 503: golf club/golf-club
+ 504: golfcart
+ 505: gondola/gondola boat
+ 506: goose
+ 507: gorilla
+ 508: gourd
+ 509: grape
+ 510: grater
+ 511: gravestone/headstone/tombstone
+ 512: gravy boat/gravy holder
+ 513: green bean
+ 514: green onion/spring onion/scallion
+ 515: griddle
+ 516: grill/grille/grillwork/radiator grille
+ 517: grits/hominy grits
+ 518: grizzly/grizzly bear
+ 519: grocery bag
+ 520: guitar
+ 521: gull/seagull
+ 522: gun
+ 523: hairbrush
+ 524: hairnet
+ 525: hairpin
+ 526: halter top
+ 527: ham/jambon/gammon
+ 528: hamburger/beefburger/burger
+ 529: hammer
+ 530: hammock
+ 531: hamper
+ 532: hamster
+ 533: hair dryer
+ 534: hand glass/hand mirror
+ 535: hand towel/face towel
+ 536: handcart/pushcart/hand truck
+ 537: handcuff
+ 538: handkerchief
+ 539: handle/grip/handgrip
+ 540: handsaw/carpenter's saw
+ 541: hardback book/hardcover book
+ 542: harmonium/organ/organ musical instrument/reed organ/reed organ musical instrument
+ 543: hat
+ 544: hatbox
+ 545: veil
+ 546: headband
+ 547: headboard
+ 548: headlight/headlamp
+ 549: headscarf
+ 550: headset
+ 551: headstall/headstall for horses/headpiece/headpiece for horses
+ 552: heart
+ 553: heater/warmer
+ 554: helicopter
+ 555: helmet
+ 556: heron
+ 557: highchair/feeding chair
+ 558: hinge
+ 559: hippopotamus
+ 560: hockey stick
+ 561: hog/pig
+ 562: home plate/home plate baseball/home base/home base baseball
+ 563: honey
+ 564: fume hood/exhaust hood
+ 565: hook
+ 566: hookah/narghile/nargileh/sheesha/shisha/water pipe
+ 567: hornet
+ 568: horse
+ 569: hose/hosepipe
+ 570: hot-air balloon
+ 571: hotplate
+ 572: hot sauce
+ 573: hourglass
+ 574: houseboat
+ 575: hummingbird
+ 576: hummus/humus/hommos/hoummos/humous
+ 577: polar bear
+ 578: icecream
+ 579: popsicle
+ 580: ice maker
+ 581: ice pack/ice bag
+ 582: ice skate
+ 583: igniter/ignitor/lighter
+ 584: inhaler/inhalator
+ 585: iPod
+ 586: iron/iron for clothing/smoothing iron/smoothing iron for clothing
+ 587: ironing board
+ 588: jacket
+ 589: jam
+ 590: jar
+ 591: jean/blue jean/denim
+ 592: jeep/landrover
+ 593: jelly bean/jelly egg
+ 594: jersey/T-shirt/tee shirt
+ 595: jet plane/jet-propelled plane
+ 596: jewel/gem/precious stone
+ 597: jewelry/jewellery
+ 598: joystick
+ 599: jumpsuit
+ 600: kayak
+ 601: keg
+ 602: kennel/doghouse
+ 603: kettle/boiler
+ 604: key
+ 605: keycard
+ 606: kilt
+ 607: kimono
+ 608: kitchen sink
+ 609: kitchen table
+ 610: kite
+ 611: kitten/kitty
+ 612: kiwi fruit
+ 613: knee pad
+ 614: knife
+ 615: knitting needle
+ 616: knob
+ 617: knocker/knocker on a door/doorknocker
+ 618: koala/koala bear
+ 619: lab coat/laboratory coat
+ 620: ladder
+ 621: ladle
+ 622: ladybug/ladybeetle/ladybird beetle
+ 623: lamb/lamb animal
+ 624: lamb-chop/lambchop
+ 625: lamp
+ 626: lamppost
+ 627: lampshade
+ 628: lantern
+ 629: lanyard/laniard
+ 630: laptop computer/notebook computer
+ 631: lasagna/lasagne
+ 632: latch
+ 633: lawn mower
+ 634: leather
+ 635: legging/legging clothing/leging/leging clothing/leg covering
+ 636: Lego/Lego set
+ 637: legume
+ 638: lemon
+ 639: lemonade
+ 640: lettuce
+ 641: license plate/numberplate
+ 642: life buoy/lifesaver/life belt/life ring
+ 643: life jacket/life vest
+ 644: lightbulb
+ 645: lightning rod/lightning conductor
+ 646: lime
+ 647: limousine
+ 648: lion
+ 649: lip balm
+ 650: liquor/spirits/hard liquor/liqueur/cordial
+ 651: lizard
+ 652: log
+ 653: lollipop
+ 654: speaker/speaker stereo equipment
+ 655: loveseat
+ 656: machine gun
+ 657: magazine
+ 658: magnet
+ 659: mail slot
+ 660: mailbox/mailbox at home/letter box/letter box at home
+ 661: mallard
+ 662: mallet
+ 663: mammoth
+ 664: manatee
+ 665: mandarin orange
+ 666: manager/through
+ 667: manhole
+ 668: map
+ 669: marker
+ 670: martini
+ 671: mascot
+ 672: mashed potato
+ 673: masher
+ 674: mask/facemask
+ 675: mast
+ 676: mat/mat gym equipment/gym mat
+ 677: matchbox
+ 678: mattress
+ 679: measuring cup
+ 680: measuring stick/ruler/ruler measuring stick/measuring rod
+ 681: meatball
+ 682: medicine
+ 683: melon
+ 684: microphone
+ 685: microscope
+ 686: microwave oven
+ 687: milestone/milepost
+ 688: milk
+ 689: milk can
+ 690: milkshake
+ 691: minivan
+ 692: mint candy
+ 693: mirror
+ 694: mitten
+ 695: mixer/mixer kitchen tool/stand mixer
+ 696: money
+ 697: monitor/monitor computer equipment
+ 698: monkey
+ 699: motor
+ 700: motor scooter/scooter
+ 701: motor vehicle/automotive vehicle
+ 702: motorcycle
+ 703: mound/mound baseball/pitcher's mound
+ 704: mouse/mouse computer equipment/computer mouse
+ 705: mousepad
+ 706: muffin
+ 707: mug
+ 708: mushroom
+ 709: music stool/piano stool
+ 710: musical instrument/instrument/instrument musical
+ 711: nailfile
+ 712: napkin/table napkin/serviette
+ 713: neckerchief
+ 714: necklace
+ 715: necktie/tie/tie necktie
+ 716: needle
+ 717: nest
+ 718: newspaper/paper/paper newspaper
+ 719: newsstand
+ 720: nightshirt/nightwear/sleepwear/nightclothes
+ 721: nosebag/nosebag for animals/feedbag
+ 722: noseband/noseband for animals/nosepiece/nosepiece for animals
+ 723: notebook
+ 724: notepad
+ 725: nut
+ 726: nutcracker
+ 727: oar
+ 728: octopus/octopus food
+ 729: octopus/octopus animal
+ 730: oil lamp/kerosene lamp/kerosine lamp
+ 731: olive oil
+ 732: omelet/omelette
+ 733: onion
+ 734: orange/orange fruit
+ 735: orange juice
+ 736: ostrich
+ 737: ottoman/pouf/pouffe/hassock
+ 738: oven
+ 739: overalls/overalls clothing
+ 740: owl
+ 741: packet
+ 742: inkpad/inking pad/stamp pad
+ 743: pad
+ 744: paddle/boat paddle
+ 745: padlock
+ 746: paintbrush
+ 747: painting
+ 748: pajamas/pyjamas
+ 749: palette/pallet
+ 750: pan/pan for cooking/cooking pan
+ 751: pan/pan metal container
+ 752: pancake
+ 753: pantyhose
+ 754: papaya
+ 755: paper plate
+ 756: paper towel
+ 757: paperback book/paper-back book/softback book/soft-cover book
+ 758: paperweight
+ 759: parachute
+ 760: parakeet/parrakeet/parroket/paraquet/paroquet/parroquet
+ 761: parasail/parasail sports
+ 762: parasol/sunshade
+ 763: parchment
+ 764: parka/anorak
+ 765: parking meter
+ 766: parrot
+ 767: passenger car/passenger car part of a train/coach/coach part of a train
+ 768: passenger ship
+ 769: passport
+ 770: pastry
+ 771: patty/patty food
+ 772: pea/pea food
+ 773: peach
+ 774: peanut butter
+ 775: pear
+ 776: peeler/peeler tool for fruit and vegetables
+ 777: wooden leg/pegleg
+ 778: pegboard
+ 779: pelican
+ 780: pen
+ 781: pencil
+ 782: pencil box/pencil case
+ 783: pencil sharpener
+ 784: pendulum
+ 785: penguin
+ 786: pennant
+ 787: penny/penny coin
+ 788: pepper/peppercorn
+ 789: pepper mill/pepper grinder
+ 790: perfume
+ 791: persimmon
+ 792: person/baby/child/boy/girl/man/woman/human
+ 793: pet
+ 794: pew/pew church bench/church bench
+ 795: phonebook/telephone book/telephone directory
+ 796: phonograph record/phonograph recording/record/record phonograph recording
+ 797: piano
+ 798: pickle
+ 799: pickup truck
+ 800: pie
+ 801: pigeon
+ 802: piggy bank/penny bank
+ 803: pillow
+ 804: pin/pin non jewelry
+ 805: pineapple
+ 806: pinecone
+ 807: ping-pong ball
+ 808: pinwheel
+ 809: tobacco pipe
+ 810: pipe/piping
+ 811: pistol/handgun
+ 812: pita/pita bread/pocket bread
+ 813: pitcher/pitcher vessel for liquid/ewer
+ 814: pitchfork
+ 815: pizza
+ 816: place mat
+ 817: plate
+ 818: platter
+ 819: playpen
+ 820: pliers/plyers
+ 821: plow/plow farm equipment/plough/plough farm equipment
+ 822: plume
+ 823: pocket watch
+ 824: pocketknife
+ 825: poker/poker fire stirring tool/stove poker/fire hook
+ 826: pole/post
+ 827: polo shirt/sport shirt
+ 828: poncho
+ 829: pony
+ 830: pool table/billiard table/snooker table
+ 831: pop/pop soda/soda/soda pop/tonic/soft drink
+ 832: postbox/postbox public/mailbox/mailbox public
+ 833: postcard/postal card/mailing-card
+ 834: poster/placard
+ 835: pot
+ 836: flowerpot
+ 837: potato
+ 838: potholder
+ 839: pottery/clayware
+ 840: pouch
+ 841: power shovel/excavator/digger
+ 842: prawn/shrimp
+ 843: pretzel
+ 844: printer/printing machine
+ 845: projectile/projectile weapon/missile
+ 846: projector
+ 847: propeller/propellor
+ 848: prune
+ 849: pudding
+ 850: puffer/puffer fish/pufferfish/blowfish/globefish
+ 851: puffin
+ 852: pug-dog
+ 853: pumpkin
+ 854: puncher
+ 855: puppet/marionette
+ 856: puppy
+ 857: quesadilla
+ 858: quiche
+ 859: quilt/comforter
+ 860: rabbit
+ 861: race car/racing car
+ 862: racket/racquet
+ 863: radar
+ 864: radiator
+ 865: radio receiver/radio set/radio/tuner/tuner radio
+ 866: radish/daikon
+ 867: raft
+ 868: rag doll
+ 869: raincoat/waterproof jacket
+ 870: ram/ram animal
+ 871: raspberry
+ 872: rat
+ 873: razorblade
+ 874: reamer/reamer juicer/juicer/juice reamer
+ 875: rearview mirror
+ 876: receipt
+ 877: recliner/reclining chair/lounger/lounger chair
+ 878: record player/phonograph/phonograph record player/turntable
+ 879: reflector
+ 880: remote control
+ 881: rhinoceros
+ 882: rib/rib food
+ 883: rifle
+ 884: ring
+ 885: river boat
+ 886: road map
+ 887: robe
+ 888: rocking chair
+ 889: rodent
+ 890: roller skate
+ 891: Rollerblade
+ 892: rolling pin
+ 893: root beer
+ 894: router/router computer equipment
+ 895: rubber band/elastic band
+ 896: runner/runner carpet
+ 897: plastic bag/paper bag
+ 898: saddle/saddle on an animal
+ 899: saddle blanket/saddlecloth/horse blanket
+ 900: saddlebag
+ 901: safety pin
+ 902: sail
+ 903: salad
+ 904: salad plate/salad bowl
+ 905: salami
+ 906: salmon/salmon fish
+ 907: salmon/salmon food
+ 908: salsa
+ 909: saltshaker
+ 910: sandal/sandal type of shoe
+ 911: sandwich
+ 912: satchel
+ 913: saucepan
+ 914: saucer
+ 915: sausage
+ 916: sawhorse/sawbuck
+ 917: saxophone
+ 918: scale/scale measuring instrument
+ 919: scarecrow/strawman
+ 920: scarf
+ 921: school bus
+ 922: scissors
+ 923: scoreboard
+ 924: scraper
+ 925: screwdriver
+ 926: scrubbing brush
+ 927: sculpture
+ 928: seabird/seafowl
+ 929: seahorse
+ 930: seaplane/hydroplane
+ 931: seashell
+ 932: sewing machine
+ 933: shaker
+ 934: shampoo
+ 935: shark
+ 936: sharpener
+ 937: Sharpie
+ 938: shaver/shaver electric/electric shaver/electric razor
+ 939: shaving cream/shaving soap
+ 940: shawl
+ 941: shears
+ 942: sheep
+ 943: shepherd dog/sheepdog
+ 944: sherbert/sherbet
+ 945: shield
+ 946: shirt
+ 947: shoe/sneaker/sneaker type of shoe/tennis shoe
+ 948: shopping bag
+ 949: shopping cart
+ 950: short pants/shorts/shorts clothing/trunks/trunks clothing
+ 951: shot glass
+ 952: shoulder bag
+ 953: shovel
+ 954: shower head
+ 955: shower cap
+ 956: shower curtain
+ 957: shredder/shredder for paper
+ 958: signboard
+ 959: silo
+ 960: sink
+ 961: skateboard
+ 962: skewer
+ 963: ski
+ 964: ski boot
+ 965: ski parka/ski jacket
+ 966: ski pole
+ 967: skirt
+ 968: skullcap
+ 969: sled/sledge/sleigh
+ 970: sleeping bag
+ 971: sling/sling bandage/triangular bandage
+ 972: slipper/slipper footwear/carpet slipper/carpet slipper footwear
+ 973: smoothie
+ 974: snake/serpent
+ 975: snowboard
+ 976: snowman
+ 977: snowmobile
+ 978: soap
+ 979: soccer ball
+ 980: sock
+ 981: sofa/couch/lounge
+ 982: softball
+ 983: solar array/solar battery/solar panel
+ 984: sombrero
+ 985: soup
+ 986: soup bowl
+ 987: soupspoon
+ 988: sour cream/soured cream
+ 989: soya milk/soybean milk/soymilk
+ 990: space shuttle
+ 991: sparkler/sparkler fireworks
+ 992: spatula
+ 993: spear/lance
+ 994: spectacles/specs/eyeglasses/glasses
+ 995: spice rack
+ 996: spider
+ 997: crawfish/crayfish
+ 998: sponge
+ 999: spoon
+ 1000: sportswear/athletic wear/activewear
+ 1001: spotlight
+ 1002: squid/squid food/calamari/calamary
+ 1003: squirrel
+ 1004: stagecoach
+ 1005: stapler/stapler stapling machine
+ 1006: starfish/sea star
+ 1007: statue/statue sculpture
+ 1008: steak/steak food
+ 1009: steak knife
+ 1010: steering wheel
+ 1011: stepladder
+ 1012: step stool
+ 1013: stereo/stereo sound system
+ 1014: stew
+ 1015: stirrer
+ 1016: stirrup
+ 1017: stool
+ 1018: stop sign
+ 1019: brake light
+ 1020: stove/kitchen stove/range/range kitchen appliance/kitchen range/cooking stove
+ 1021: strainer
+ 1022: strap
+ 1023: straw/straw for drinking/drinking straw
+ 1024: strawberry
+ 1025: street sign
+ 1026: streetlight/street lamp
+ 1027: string cheese
+ 1028: stylus
+ 1029: subwoofer
+ 1030: sugar bowl
+ 1031: sugarcane/sugarcane plant
+ 1032: suit/suit clothing
+ 1033: sunflower
+ 1034: sunglasses
+ 1035: sunhat
+ 1036: surfboard
+ 1037: sushi
+ 1038: mop
+ 1039: sweat pants
+ 1040: sweatband
+ 1041: sweater
+ 1042: sweatshirt
+ 1043: sweet potato
+ 1044: swimsuit/swimwear/bathing suit/swimming costume/bathing costume/swimming trunks/bathing trunks
+ 1045: sword
+ 1046: syringe
+ 1047: Tabasco sauce
+ 1048: table-tennis table/ping-pong table
+ 1049: table
+ 1050: table lamp
+ 1051: tablecloth
+ 1052: tachometer
+ 1053: taco
+ 1054: tag
+ 1055: taillight/rear light
+ 1056: tambourine
+ 1057: army tank/armored combat vehicle/armoured combat vehicle
+ 1058: tank/tank storage vessel/storage tank
+ 1059: tank top/tank top clothing
+ 1060: tape/tape sticky cloth or paper
+ 1061: tape measure/measuring tape
+ 1062: tapestry
+ 1063: tarp
+ 1064: tartan/plaid
+ 1065: tassel
+ 1066: tea bag
+ 1067: teacup
+ 1068: teakettle
+ 1069: teapot
+ 1070: teddy bear
+ 1071: telephone/phone/telephone set
+ 1072: telephone booth/phone booth/call box/telephone box/telephone kiosk
+ 1073: telephone pole/telegraph pole/telegraph post
+ 1074: telephoto lens/zoom lens
+ 1075: television camera/tv camera
+ 1076: television set/tv/tv set
+ 1077: tennis ball
+ 1078: tennis racket
+ 1079: tequila
+ 1080: thermometer
+ 1081: thermos bottle
+ 1082: thermostat
+ 1083: thimble
+ 1084: thread/yarn
+ 1085: thumbtack/drawing pin/pushpin
+ 1086: tiara
+ 1087: tiger
+ 1088: tights/tights clothing/leotards
+ 1089: timer/stopwatch
+ 1090: tinfoil
+ 1091: tinsel
+ 1092: tissue paper
+ 1093: toast/toast food
+ 1094: toaster
+ 1095: toaster oven
+ 1096: toilet
+ 1097: toilet tissue/toilet paper/bathroom tissue
+ 1098: tomato
+ 1099: tongs
+ 1100: toolbox
+ 1101: toothbrush
+ 1102: toothpaste
+ 1103: toothpick
+ 1104: cover
+ 1105: tortilla
+ 1106: tow truck
+ 1107: towel
+ 1108: towel rack/towel rail/towel bar
+ 1109: toy
+ 1110: tractor/tractor farm equipment
+ 1111: traffic light
+ 1112: dirt bike
+ 1113: trailer truck/tractor trailer/trucking rig/articulated lorry/semi truck
+ 1114: train/train railroad vehicle/railroad train
+ 1115: trampoline
+ 1116: tray
+ 1117: trench coat
+ 1118: triangle/triangle musical instrument
+ 1119: tricycle
+ 1120: tripod
+ 1121: trousers/pants/pants clothing
+ 1122: truck
+ 1123: truffle/truffle chocolate/chocolate truffle
+ 1124: trunk
+ 1125: vat
+ 1126: turban
+ 1127: turkey/turkey food
+ 1128: turnip
+ 1129: turtle
+ 1130: turtleneck/turtleneck clothing/polo-neck
+ 1131: typewriter
+ 1132: umbrella
+ 1133: underwear/underclothes/underclothing/underpants
+ 1134: unicycle
+ 1135: urinal
+ 1136: urn
+ 1137: vacuum cleaner
+ 1138: vase
+ 1139: vending machine
+ 1140: vent/blowhole/air vent
+ 1141: vest/waistcoat
+ 1142: videotape
+ 1143: vinegar
+ 1144: violin/fiddle
+ 1145: vodka
+ 1146: volleyball
+ 1147: vulture
+ 1148: waffle
+ 1149: waffle iron
+ 1150: wagon
+ 1151: wagon wheel
+ 1152: walking stick
+ 1153: wall clock
+ 1154: wall socket/wall plug/electric outlet/electrical outlet/outlet/electric receptacle
+ 1155: wallet/billfold
+ 1156: walrus
+ 1157: wardrobe
+ 1158: washbasin/basin/basin for washing/washbowl/washstand/handbasin
+ 1159: automatic washer/washing machine
+ 1160: watch/wristwatch
+ 1161: water bottle
+ 1162: water cooler
+ 1163: water faucet/water tap/tap/tap water faucet
+ 1164: water heater/hot-water heater
+ 1165: water jug
+ 1166: water gun/squirt gun
+ 1167: water scooter/sea scooter/jet ski
+ 1168: water ski
+ 1169: water tower
+ 1170: watering can
+ 1171: watermelon
+ 1172: weathervane/vane/vane weathervane/wind vane
+ 1173: webcam
+ 1174: wedding cake/bridecake
+ 1175: wedding ring/wedding band
+ 1176: wet suit
+ 1177: wheel
+ 1178: wheelchair
+ 1179: whipped cream
+ 1180: whistle
+ 1181: wig
+ 1182: wind chime
+ 1183: windmill
+ 1184: window box/window box for plants
+ 1185: windshield wiper/windscreen wiper/wiper/wiper for windshield or screen
+ 1186: windsock/air sock/air-sleeve/wind sleeve/wind cone
+ 1187: wine bottle
+ 1188: wine bucket/wine cooler
+ 1189: wineglass
+ 1190: blinder/blinder for horses
+ 1191: wok
+ 1192: wolf
+ 1193: wooden spoon
+ 1194: wreath
+ 1195: wrench/spanner
+ 1196: wristband
+ 1197: wristlet/wrist band
+ 1198: yacht
+ 1199: yogurt/yoghurt/yoghourt
+ 1200: yoke/yoke animal equipment
+ 1201: zebra
+ 1202: zucchini/courgette
+
+# Download script/URL (optional)
+download: |
+ from ultralytics.utils.downloads import download
+ from pathlib import Path
+
+ # Download labels
+ dir = Path(yaml['path']) # dataset root dir
+ url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
+ urls = [url + 'lvis-labels-segments.zip'] # labels
+ download(urls, dir=dir.parent)
+ # Download data
+ urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images
+ 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images
+ 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional)
+ download(urls, dir=dir / 'images', threads=3)
diff --git a/ultralytics/cfg/datasets/medical-pills.yaml b/ultralytics/cfg/datasets/medical-pills.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..25507c8b9bef023c866aa6d6d41f8a3a6bf0958a
--- /dev/null
+++ b/ultralytics/cfg/datasets/medical-pills.yaml
@@ -0,0 +1,22 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Medical-pills dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/medical-pills/
+# Example usage: yolo train data=medical-pills.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── medical-pills ← downloads here (8.19 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/medical-pills # dataset root dir
+train: train/images # train images (relative to 'path') 92 images
+val: valid/images # val images (relative to 'path') 23 images
+test: # test images (relative to 'path')
+
+# Classes
+names:
+ 0: pill
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/medical-pills.zip
diff --git a/ultralytics/cfg/datasets/open-images-v7.yaml b/ultralytics/cfg/datasets/open-images-v7.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6bd4e0bdcf588b0736dbfe33bb52edf1ea4ea3fc
--- /dev/null
+++ b/ultralytics/cfg/datasets/open-images-v7.yaml
@@ -0,0 +1,661 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Open Images v7 dataset https://storage.googleapis.com/openimages/web/index.html by Google
+# Documentation: https://docs.ultralytics.com/datasets/detect/open-images-v7/
+# Example usage: yolo train data=open-images-v7.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── open-images-v7 ← downloads here (561 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/open-images-v7 # dataset root dir
+train: images/train # train images (relative to 'path') 1743042 images
+val: images/val # val images (relative to 'path') 41620 images
+test: # test images (optional)
+
+# Classes
+names:
+ 0: Accordion
+ 1: Adhesive tape
+ 2: Aircraft
+ 3: Airplane
+ 4: Alarm clock
+ 5: Alpaca
+ 6: Ambulance
+ 7: Animal
+ 8: Ant
+ 9: Antelope
+ 10: Apple
+ 11: Armadillo
+ 12: Artichoke
+ 13: Auto part
+ 14: Axe
+ 15: Backpack
+ 16: Bagel
+ 17: Baked goods
+ 18: Balance beam
+ 19: Ball
+ 20: Balloon
+ 21: Banana
+ 22: Band-aid
+ 23: Banjo
+ 24: Barge
+ 25: Barrel
+ 26: Baseball bat
+ 27: Baseball glove
+ 28: Bat (Animal)
+ 29: Bathroom accessory
+ 30: Bathroom cabinet
+ 31: Bathtub
+ 32: Beaker
+ 33: Bear
+ 34: Bed
+ 35: Bee
+ 36: Beehive
+ 37: Beer
+ 38: Beetle
+ 39: Bell pepper
+ 40: Belt
+ 41: Bench
+ 42: Bicycle
+ 43: Bicycle helmet
+ 44: Bicycle wheel
+ 45: Bidet
+ 46: Billboard
+ 47: Billiard table
+ 48: Binoculars
+ 49: Bird
+ 50: Blender
+ 51: Blue jay
+ 52: Boat
+ 53: Bomb
+ 54: Book
+ 55: Bookcase
+ 56: Boot
+ 57: Bottle
+ 58: Bottle opener
+ 59: Bow and arrow
+ 60: Bowl
+ 61: Bowling equipment
+ 62: Box
+ 63: Boy
+ 64: Brassiere
+ 65: Bread
+ 66: Briefcase
+ 67: Broccoli
+ 68: Bronze sculpture
+ 69: Brown bear
+ 70: Building
+ 71: Bull
+ 72: Burrito
+ 73: Bus
+ 74: Bust
+ 75: Butterfly
+ 76: Cabbage
+ 77: Cabinetry
+ 78: Cake
+ 79: Cake stand
+ 80: Calculator
+ 81: Camel
+ 82: Camera
+ 83: Can opener
+ 84: Canary
+ 85: Candle
+ 86: Candy
+ 87: Cannon
+ 88: Canoe
+ 89: Cantaloupe
+ 90: Car
+ 91: Carnivore
+ 92: Carrot
+ 93: Cart
+ 94: Cassette deck
+ 95: Castle
+ 96: Cat
+ 97: Cat furniture
+ 98: Caterpillar
+ 99: Cattle
+ 100: Ceiling fan
+ 101: Cello
+ 102: Centipede
+ 103: Chainsaw
+ 104: Chair
+ 105: Cheese
+ 106: Cheetah
+ 107: Chest of drawers
+ 108: Chicken
+ 109: Chime
+ 110: Chisel
+ 111: Chopsticks
+ 112: Christmas tree
+ 113: Clock
+ 114: Closet
+ 115: Clothing
+ 116: Coat
+ 117: Cocktail
+ 118: Cocktail shaker
+ 119: Coconut
+ 120: Coffee
+ 121: Coffee cup
+ 122: Coffee table
+ 123: Coffeemaker
+ 124: Coin
+ 125: Common fig
+ 126: Common sunflower
+ 127: Computer keyboard
+ 128: Computer monitor
+ 129: Computer mouse
+ 130: Container
+ 131: Convenience store
+ 132: Cookie
+ 133: Cooking spray
+ 134: Corded phone
+ 135: Cosmetics
+ 136: Couch
+ 137: Countertop
+ 138: Cowboy hat
+ 139: Crab
+ 140: Cream
+ 141: Cricket ball
+ 142: Crocodile
+ 143: Croissant
+ 144: Crown
+ 145: Crutch
+ 146: Cucumber
+ 147: Cupboard
+ 148: Curtain
+ 149: Cutting board
+ 150: Dagger
+ 151: Dairy Product
+ 152: Deer
+ 153: Desk
+ 154: Dessert
+ 155: Diaper
+ 156: Dice
+ 157: Digital clock
+ 158: Dinosaur
+ 159: Dishwasher
+ 160: Dog
+ 161: Dog bed
+ 162: Doll
+ 163: Dolphin
+ 164: Door
+ 165: Door handle
+ 166: Doughnut
+ 167: Dragonfly
+ 168: Drawer
+ 169: Dress
+ 170: Drill (Tool)
+ 171: Drink
+ 172: Drinking straw
+ 173: Drum
+ 174: Duck
+ 175: Dumbbell
+ 176: Eagle
+ 177: Earrings
+ 178: Egg (Food)
+ 179: Elephant
+ 180: Envelope
+ 181: Eraser
+ 182: Face powder
+ 183: Facial tissue holder
+ 184: Falcon
+ 185: Fashion accessory
+ 186: Fast food
+ 187: Fax
+ 188: Fedora
+ 189: Filing cabinet
+ 190: Fire hydrant
+ 191: Fireplace
+ 192: Fish
+ 193: Flag
+ 194: Flashlight
+ 195: Flower
+ 196: Flowerpot
+ 197: Flute
+ 198: Flying disc
+ 199: Food
+ 200: Food processor
+ 201: Football
+ 202: Football helmet
+ 203: Footwear
+ 204: Fork
+ 205: Fountain
+ 206: Fox
+ 207: French fries
+ 208: French horn
+ 209: Frog
+ 210: Fruit
+ 211: Frying pan
+ 212: Furniture
+ 213: Garden Asparagus
+ 214: Gas stove
+ 215: Giraffe
+ 216: Girl
+ 217: Glasses
+ 218: Glove
+ 219: Goat
+ 220: Goggles
+ 221: Goldfish
+ 222: Golf ball
+ 223: Golf cart
+ 224: Gondola
+ 225: Goose
+ 226: Grape
+ 227: Grapefruit
+ 228: Grinder
+ 229: Guacamole
+ 230: Guitar
+ 231: Hair dryer
+ 232: Hair spray
+ 233: Hamburger
+ 234: Hammer
+ 235: Hamster
+ 236: Hand dryer
+ 237: Handbag
+ 238: Handgun
+ 239: Harbor seal
+ 240: Harmonica
+ 241: Harp
+ 242: Harpsichord
+ 243: Hat
+ 244: Headphones
+ 245: Heater
+ 246: Hedgehog
+ 247: Helicopter
+ 248: Helmet
+ 249: High heels
+ 250: Hiking equipment
+ 251: Hippopotamus
+ 252: Home appliance
+ 253: Honeycomb
+ 254: Horizontal bar
+ 255: Horse
+ 256: Hot dog
+ 257: House
+ 258: Houseplant
+ 259: Human arm
+ 260: Human beard
+ 261: Human body
+ 262: Human ear
+ 263: Human eye
+ 264: Human face
+ 265: Human foot
+ 266: Human hair
+ 267: Human hand
+ 268: Human head
+ 269: Human leg
+ 270: Human mouth
+ 271: Human nose
+ 272: Humidifier
+ 273: Ice cream
+ 274: Indoor rower
+ 275: Infant bed
+ 276: Insect
+ 277: Invertebrate
+ 278: Ipod
+ 279: Isopod
+ 280: Jacket
+ 281: Jacuzzi
+ 282: Jaguar (Animal)
+ 283: Jeans
+ 284: Jellyfish
+ 285: Jet ski
+ 286: Jug
+ 287: Juice
+ 288: Kangaroo
+ 289: Kettle
+ 290: Kitchen & dining room table
+ 291: Kitchen appliance
+ 292: Kitchen knife
+ 293: Kitchen utensil
+ 294: Kitchenware
+ 295: Kite
+ 296: Knife
+ 297: Koala
+ 298: Ladder
+ 299: Ladle
+ 300: Ladybug
+ 301: Lamp
+ 302: Land vehicle
+ 303: Lantern
+ 304: Laptop
+ 305: Lavender (Plant)
+ 306: Lemon
+ 307: Leopard
+ 308: Light bulb
+ 309: Light switch
+ 310: Lighthouse
+ 311: Lily
+ 312: Limousine
+ 313: Lion
+ 314: Lipstick
+ 315: Lizard
+ 316: Lobster
+ 317: Loveseat
+ 318: Luggage and bags
+ 319: Lynx
+ 320: Magpie
+ 321: Mammal
+ 322: Man
+ 323: Mango
+ 324: Maple
+ 325: Maracas
+ 326: Marine invertebrates
+ 327: Marine mammal
+ 328: Measuring cup
+ 329: Mechanical fan
+ 330: Medical equipment
+ 331: Microphone
+ 332: Microwave oven
+ 333: Milk
+ 334: Miniskirt
+ 335: Mirror
+ 336: Missile
+ 337: Mixer
+ 338: Mixing bowl
+ 339: Mobile phone
+ 340: Monkey
+ 341: Moths and butterflies
+ 342: Motorcycle
+ 343: Mouse
+ 344: Muffin
+ 345: Mug
+ 346: Mule
+ 347: Mushroom
+ 348: Musical instrument
+ 349: Musical keyboard
+ 350: Nail (Construction)
+ 351: Necklace
+ 352: Nightstand
+ 353: Oboe
+ 354: Office building
+ 355: Office supplies
+ 356: Orange
+ 357: Organ (Musical Instrument)
+ 358: Ostrich
+ 359: Otter
+ 360: Oven
+ 361: Owl
+ 362: Oyster
+ 363: Paddle
+ 364: Palm tree
+ 365: Pancake
+ 366: Panda
+ 367: Paper cutter
+ 368: Paper towel
+ 369: Parachute
+ 370: Parking meter
+ 371: Parrot
+ 372: Pasta
+ 373: Pastry
+ 374: Peach
+ 375: Pear
+ 376: Pen
+ 377: Pencil case
+ 378: Pencil sharpener
+ 379: Penguin
+ 380: Perfume
+ 381: Person
+ 382: Personal care
+ 383: Personal flotation device
+ 384: Piano
+ 385: Picnic basket
+ 386: Picture frame
+ 387: Pig
+ 388: Pillow
+ 389: Pineapple
+ 390: Pitcher (Container)
+ 391: Pizza
+ 392: Pizza cutter
+ 393: Plant
+ 394: Plastic bag
+ 395: Plate
+ 396: Platter
+ 397: Plumbing fixture
+ 398: Polar bear
+ 399: Pomegranate
+ 400: Popcorn
+ 401: Porch
+ 402: Porcupine
+ 403: Poster
+ 404: Potato
+ 405: Power plugs and sockets
+ 406: Pressure cooker
+ 407: Pretzel
+ 408: Printer
+ 409: Pumpkin
+ 410: Punching bag
+ 411: Rabbit
+ 412: Raccoon
+ 413: Racket
+ 414: Radish
+ 415: Ratchet (Device)
+ 416: Raven
+ 417: Rays and skates
+ 418: Red panda
+ 419: Refrigerator
+ 420: Remote control
+ 421: Reptile
+ 422: Rhinoceros
+ 423: Rifle
+ 424: Ring binder
+ 425: Rocket
+ 426: Roller skates
+ 427: Rose
+ 428: Rugby ball
+ 429: Ruler
+ 430: Salad
+ 431: Salt and pepper shakers
+ 432: Sandal
+ 433: Sandwich
+ 434: Saucer
+ 435: Saxophone
+ 436: Scale
+ 437: Scarf
+ 438: Scissors
+ 439: Scoreboard
+ 440: Scorpion
+ 441: Screwdriver
+ 442: Sculpture
+ 443: Sea lion
+ 444: Sea turtle
+ 445: Seafood
+ 446: Seahorse
+ 447: Seat belt
+ 448: Segway
+ 449: Serving tray
+ 450: Sewing machine
+ 451: Shark
+ 452: Sheep
+ 453: Shelf
+ 454: Shellfish
+ 455: Shirt
+ 456: Shorts
+ 457: Shotgun
+ 458: Shower
+ 459: Shrimp
+ 460: Sink
+ 461: Skateboard
+ 462: Ski
+ 463: Skirt
+ 464: Skull
+ 465: Skunk
+ 466: Skyscraper
+ 467: Slow cooker
+ 468: Snack
+ 469: Snail
+ 470: Snake
+ 471: Snowboard
+ 472: Snowman
+ 473: Snowmobile
+ 474: Snowplow
+ 475: Soap dispenser
+ 476: Sock
+ 477: Sofa bed
+ 478: Sombrero
+ 479: Sparrow
+ 480: Spatula
+ 481: Spice rack
+ 482: Spider
+ 483: Spoon
+ 484: Sports equipment
+ 485: Sports uniform
+ 486: Squash (Plant)
+ 487: Squid
+ 488: Squirrel
+ 489: Stairs
+ 490: Stapler
+ 491: Starfish
+ 492: Stationary bicycle
+ 493: Stethoscope
+ 494: Stool
+ 495: Stop sign
+ 496: Strawberry
+ 497: Street light
+ 498: Stretcher
+ 499: Studio couch
+ 500: Submarine
+ 501: Submarine sandwich
+ 502: Suit
+ 503: Suitcase
+ 504: Sun hat
+ 505: Sunglasses
+ 506: Surfboard
+ 507: Sushi
+ 508: Swan
+ 509: Swim cap
+ 510: Swimming pool
+ 511: Swimwear
+ 512: Sword
+ 513: Syringe
+ 514: Table
+ 515: Table tennis racket
+ 516: Tablet computer
+ 517: Tableware
+ 518: Taco
+ 519: Tank
+ 520: Tap
+ 521: Tart
+ 522: Taxi
+ 523: Tea
+ 524: Teapot
+ 525: Teddy bear
+ 526: Telephone
+ 527: Television
+ 528: Tennis ball
+ 529: Tennis racket
+ 530: Tent
+ 531: Tiara
+ 532: Tick
+ 533: Tie
+ 534: Tiger
+ 535: Tin can
+ 536: Tire
+ 537: Toaster
+ 538: Toilet
+ 539: Toilet paper
+ 540: Tomato
+ 541: Tool
+ 542: Toothbrush
+ 543: Torch
+ 544: Tortoise
+ 545: Towel
+ 546: Tower
+ 547: Toy
+ 548: Traffic light
+ 549: Traffic sign
+ 550: Train
+ 551: Training bench
+ 552: Treadmill
+ 553: Tree
+ 554: Tree house
+ 555: Tripod
+ 556: Trombone
+ 557: Trousers
+ 558: Truck
+ 559: Trumpet
+ 560: Turkey
+ 561: Turtle
+ 562: Umbrella
+ 563: Unicycle
+ 564: Van
+ 565: Vase
+ 566: Vegetable
+ 567: Vehicle
+ 568: Vehicle registration plate
+ 569: Violin
+ 570: Volleyball (Ball)
+ 571: Waffle
+ 572: Waffle iron
+ 573: Wall clock
+ 574: Wardrobe
+ 575: Washing machine
+ 576: Waste container
+ 577: Watch
+ 578: Watercraft
+ 579: Watermelon
+ 580: Weapon
+ 581: Whale
+ 582: Wheel
+ 583: Wheelchair
+ 584: Whisk
+ 585: Whiteboard
+ 586: Willow
+ 587: Window
+ 588: Window blind
+ 589: Wine
+ 590: Wine glass
+ 591: Wine rack
+ 592: Winter melon
+ 593: Wok
+ 594: Woman
+ 595: Wood-burning stove
+ 596: Woodpecker
+ 597: Worm
+ 598: Wrench
+ 599: Zebra
+ 600: Zucchini
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ from ultralytics.utils import LOGGER, SETTINGS, Path, is_ubuntu, get_ubuntu_version
+ from ultralytics.utils.checks import check_requirements, check_version
+
+ check_requirements('fiftyone')
+ if is_ubuntu() and check_version(get_ubuntu_version(), '>=22.04'):
+ # Ubuntu>=22.04 patch https://github.com/voxel51/fiftyone/issues/2961#issuecomment-1666519347
+ check_requirements('fiftyone-db-ubuntu2204')
+
+ import fiftyone as fo
+ import fiftyone.zoo as foz
+ import warnings
+
+ name = 'open-images-v7'
+ fraction = 1.0 # fraction of full dataset to use
+ LOGGER.warning('WARNING ⚠️ Open Images V7 dataset requires at least **561 GB of free space. Starting download...')
+ for split in 'train', 'validation': # 1743042 train, 41620 val images
+ train = split == 'train'
+
+ # Load Open Images dataset
+ dataset = foz.load_zoo_dataset(name,
+ split=split,
+ label_types=['detections'],
+ dataset_dir=Path(SETTINGS['datasets_dir']) / 'fiftyone' / name,
+ max_samples=round((1743042 if train else 41620) * fraction))
+
+ # Define classes
+ if train:
+ classes = dataset.default_classes # all classes
+ # classes = dataset.distinct('ground_truth.detections.label') # only observed classes
+
+ # Export to YOLO format
+ with warnings.catch_warnings():
+ warnings.filterwarnings("ignore", category=UserWarning, module="fiftyone.utils.yolo")
+ dataset.export(export_dir=str(Path(SETTINGS['datasets_dir']) / name),
+ dataset_type=fo.types.YOLOv5Dataset,
+ label_field='ground_truth',
+ split='val' if split == 'validation' else split,
+ classes=classes,
+ overwrite=train)
diff --git a/ultralytics/cfg/datasets/package-seg.yaml b/ultralytics/cfg/datasets/package-seg.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..433ca04c7fe58a6f7da6130acda50e335901fbb1
--- /dev/null
+++ b/ultralytics/cfg/datasets/package-seg.yaml
@@ -0,0 +1,22 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Package-seg dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/segment/package-seg/
+# Example usage: yolo train data=package-seg.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── package-seg ← downloads here (102 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/package-seg # dataset root dir
+train: train/images # train images (relative to 'path') 1920 images
+val: valid/images # val images (relative to 'path') 89 images
+test: test/images # test images (relative to 'path') 188 images
+
+# Classes
+names:
+ 0: package
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/package-seg.zip
diff --git a/ultralytics/cfg/datasets/signature.yaml b/ultralytics/cfg/datasets/signature.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5c9d5c338e95b566cd8e6f56294e32dc6c5ab323
--- /dev/null
+++ b/ultralytics/cfg/datasets/signature.yaml
@@ -0,0 +1,21 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Signature dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/signature/
+# Example usage: yolo train data=signature.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── signature ← downloads here (11.2 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/signature # dataset root dir
+train: train/images # train images (relative to 'path') 143 images
+val: valid/images # val images (relative to 'path') 35 images
+
+# Classes
+names:
+ 0: signature
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/signature.zip
diff --git a/ultralytics/cfg/datasets/tiger-pose.yaml b/ultralytics/cfg/datasets/tiger-pose.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2b3f7b71761e475cff2aa3c97c8301c848e0844f
--- /dev/null
+++ b/ultralytics/cfg/datasets/tiger-pose.yaml
@@ -0,0 +1,25 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Tiger Pose dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/pose/tiger-pose/
+# Example usage: yolo train data=tiger-pose.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── tiger-pose ← downloads here (75.3 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/tiger-pose # dataset root dir
+train: train # train images (relative to 'path') 210 images
+val: val # val images (relative to 'path') 53 images
+
+# Keypoints
+kpt_shape: [12, 2] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+flip_idx: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
+
+# Classes
+names:
+ 0: tiger
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/tiger-pose.zip
diff --git a/ultralytics/cfg/datasets/xView.yaml b/ultralytics/cfg/datasets/xView.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ccef985974b74a22f0614da384210e32b0af59d4
--- /dev/null
+++ b/ultralytics/cfg/datasets/xView.yaml
@@ -0,0 +1,153 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
+# -------- DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command! --------
+# Documentation: https://docs.ultralytics.com/datasets/detect/xview/
+# Example usage: yolo train data=xView.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+# └── xView ← downloads here (20.7 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/xView # dataset root dir
+train: images/autosplit_train.txt # train images (relative to 'path') 90% of 847 train images
+val: images/autosplit_val.txt # train images (relative to 'path') 10% of 847 train images
+
+# Classes
+names:
+ 0: Fixed-wing Aircraft
+ 1: Small Aircraft
+ 2: Cargo Plane
+ 3: Helicopter
+ 4: Passenger Vehicle
+ 5: Small Car
+ 6: Bus
+ 7: Pickup Truck
+ 8: Utility Truck
+ 9: Truck
+ 10: Cargo Truck
+ 11: Truck w/Box
+ 12: Truck Tractor
+ 13: Trailer
+ 14: Truck w/Flatbed
+ 15: Truck w/Liquid
+ 16: Crane Truck
+ 17: Railway Vehicle
+ 18: Passenger Car
+ 19: Cargo Car
+ 20: Flat Car
+ 21: Tank car
+ 22: Locomotive
+ 23: Maritime Vessel
+ 24: Motorboat
+ 25: Sailboat
+ 26: Tugboat
+ 27: Barge
+ 28: Fishing Vessel
+ 29: Ferry
+ 30: Yacht
+ 31: Container Ship
+ 32: Oil Tanker
+ 33: Engineering Vehicle
+ 34: Tower crane
+ 35: Container Crane
+ 36: Reach Stacker
+ 37: Straddle Carrier
+ 38: Mobile Crane
+ 39: Dump Truck
+ 40: Haul Truck
+ 41: Scraper/Tractor
+ 42: Front loader/Bulldozer
+ 43: Excavator
+ 44: Cement Mixer
+ 45: Ground Grader
+ 46: Hut/Tent
+ 47: Shed
+ 48: Building
+ 49: Aircraft Hangar
+ 50: Damaged Building
+ 51: Facility
+ 52: Construction Site
+ 53: Vehicle Lot
+ 54: Helipad
+ 55: Storage Tank
+ 56: Shipping container lot
+ 57: Shipping Container
+ 58: Pylon
+ 59: Tower
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ import json
+ import os
+ from pathlib import Path
+
+ import numpy as np
+ from PIL import Image
+ from tqdm import tqdm
+
+ from ultralytics.data.utils import autosplit
+ from ultralytics.utils.ops import xyxy2xywhn
+
+
+ def convert_labels(fname=Path('xView/xView_train.geojson')):
+ # Convert xView geoJSON labels to YOLO format
+ path = fname.parent
+ with open(fname) as f:
+ print(f'Loading {fname}...')
+ data = json.load(f)
+
+ # Make dirs
+ labels = Path(path / 'labels' / 'train')
+ os.system(f'rm -rf {labels}')
+ labels.mkdir(parents=True, exist_ok=True)
+
+ # xView classes 11-94 to 0-59
+ xview_class2index = [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 0, 1, 2, -1, 3, -1, 4, 5, 6, 7, 8, -1, 9, 10, 11,
+ 12, 13, 14, 15, -1, -1, 16, 17, 18, 19, 20, 21, 22, -1, 23, 24, 25, -1, 26, 27, -1, 28, -1,
+ 29, 30, 31, 32, 33, 34, 35, 36, 37, -1, 38, 39, 40, 41, 42, 43, 44, 45, -1, -1, -1, -1, 46,
+ 47, 48, 49, -1, 50, 51, -1, 52, -1, -1, -1, 53, 54, -1, 55, -1, -1, 56, -1, 57, -1, 58, 59]
+
+ shapes = {}
+ for feature in tqdm(data['features'], desc=f'Converting {fname}'):
+ p = feature['properties']
+ if p['bounds_imcoords']:
+ id = p['image_id']
+ file = path / 'train_images' / id
+ if file.exists(): # 1395.tif missing
+ try:
+ box = np.array([int(num) for num in p['bounds_imcoords'].split(",")])
+ assert box.shape[0] == 4, f'incorrect box shape {box.shape[0]}'
+ cls = p['type_id']
+ cls = xview_class2index[int(cls)] # xView class to 0-60
+ assert 59 >= cls >= 0, f'incorrect class index {cls}'
+
+ # Write YOLO label
+ if id not in shapes:
+ shapes[id] = Image.open(file).size
+ box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True)
+ with open((labels / id).with_suffix('.txt'), 'a') as f:
+ f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n") # write label.txt
+ except Exception as e:
+ print(f'WARNING: skipping one label for {file}: {e}')
+
+
+ # Download manually from https://challenge.xviewdataset.org
+ dir = Path(yaml['path']) # dataset root dir
+ # urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip', # train labels
+ # 'https://d307kc0mrhucc3.cloudfront.net/train_images.zip', # 15G, 847 train images
+ # 'https://d307kc0mrhucc3.cloudfront.net/val_images.zip'] # 5G, 282 val images (no labels)
+ # download(urls, dir=dir)
+
+ # Convert labels
+ convert_labels(dir / 'xView_train.geojson')
+
+ # Move images
+ images = Path(dir / 'images')
+ images.mkdir(parents=True, exist_ok=True)
+ Path(dir / 'train_images').rename(dir / 'images' / 'train')
+ Path(dir / 'val_images').rename(dir / 'images' / 'val')
+
+ # Split
+ autosplit(dir / 'images' / 'train')
diff --git a/ultralytics/cfg/default.yaml b/ultralytics/cfg/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7b67442bc68436d9e30df9c77c9219ed4d101a5b
--- /dev/null
+++ b/ultralytics/cfg/default.yaml
@@ -0,0 +1,130 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Global configuration YAML with settings and hyperparameters for YOLO training, validation, prediction and export
+# For documentation see https://docs.ultralytics.com/usage/cfg/
+
+task: detect # (str) YOLO task, i.e. detect, segment, classify, pose, obb
+mode: train # (str) YOLO mode, i.e. train, val, predict, export, track, benchmark
+
+# Train settings -------------------------------------------------------------------------------------------------------
+model: # (str, optional) path to model file, i.e. yolov8n.pt, yolov8n.yaml
+data: # (str, optional) path to data file, i.e. coco8.yaml
+epochs: 100 # (int) number of epochs to train for
+time: # (float, optional) number of hours to train for, overrides epochs if supplied
+patience: 100 # (int) epochs to wait for no observable improvement for early stopping of training
+batch: 16 # (int) number of images per batch (-1 for AutoBatch)
+imgsz: 640 # (int | list) input images size as int for train and val modes, or list[h,w] for predict and export modes
+save: True # (bool) save train checkpoints and predict results
+save_period: -1 # (int) Save checkpoint every x epochs (disabled if < 1)
+cache: False # (bool) True/ram, disk or False. Use cache for data loading
+device: # (int | str | list, optional) device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
+workers: 8 # (int) number of worker threads for data loading (per RANK if DDP)
+project: # (str, optional) project name
+name: # (str, optional) experiment name, results saved to 'project/name' directory
+exist_ok: False # (bool) whether to overwrite existing experiment
+pretrained: True # (bool | str) whether to use a pretrained model (bool) or a model to load weights from (str)
+optimizer: auto # (str) optimizer to use, choices=[SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto]
+verbose: True # (bool) whether to print verbose output
+seed: 0 # (int) random seed for reproducibility
+deterministic: True # (bool) whether to enable deterministic mode
+single_cls: False # (bool) train multi-class data as single-class
+rect: False # (bool) rectangular training if mode='train' or rectangular validation if mode='val'
+cos_lr: False # (bool) use cosine learning rate scheduler
+close_mosaic: 10 # (int) disable mosaic augmentation for final epochs (0 to disable)
+resume: False # (bool) resume training from last checkpoint
+amp: True # (bool) Automatic Mixed Precision (AMP) training, choices=[True, False], True runs AMP check
+fraction: 1.0 # (float) dataset fraction to train on (default is 1.0, all images in train set)
+profile: False # (bool) profile ONNX and TensorRT speeds during training for loggers
+freeze: None # (int | list, optional) freeze first n layers, or freeze list of layer indices during training
+multi_scale: False # (bool) Whether to use multiscale during training
+# Segmentation
+overlap_mask: True # (bool) merge object masks into a single image mask during training (segment train only)
+mask_ratio: 4 # (int) mask downsample ratio (segment train only)
+# Classification
+dropout: 0.0 # (float) use dropout regularization (classify train only)
+
+# Val/Test settings ----------------------------------------------------------------------------------------------------
+val: True # (bool) validate/test during training
+split: val # (str) dataset split to use for validation, i.e. 'val', 'test' or 'train'
+save_json: False # (bool) save results to JSON file
+save_hybrid: False # (bool) save hybrid version of labels (labels + additional predictions)
+conf: # (float, optional) object confidence threshold for detection (default 0.25 predict, 0.001 val)
+iou: 0.7 # (float) intersection over union (IoU) threshold for NMS
+max_det: 300 # (int) maximum number of detections per image
+half: False # (bool) use half precision (FP16)
+dnn: False # (bool) use OpenCV DNN for ONNX inference
+plots: True # (bool) save plots and images during train/val
+
+# Predict settings -----------------------------------------------------------------------------------------------------
+source: # (str, optional) source directory for images or videos
+vid_stride: 1 # (int) video frame-rate stride
+stream_buffer: False # (bool) buffer all streaming frames (True) or return the most recent frame (False)
+visualize: False # (bool) visualize model features
+augment: False # (bool) apply image augmentation to prediction sources
+agnostic_nms: False # (bool) class-agnostic NMS
+classes: # (int | list[int], optional) filter results by class, i.e. classes=0, or classes=[0,2,3]
+retina_masks: False # (bool) use high-resolution segmentation masks
+embed: # (list[int], optional) return feature vectors/embeddings from given layers
+
+# Visualize settings ---------------------------------------------------------------------------------------------------
+show: False # (bool) show predicted images and videos if environment allows
+save_frames: False # (bool) save predicted individual video frames
+save_txt: False # (bool) save results as .txt file
+save_conf: False # (bool) save results with confidence scores
+save_crop: False # (bool) save cropped images with results
+show_labels: True # (bool) show prediction labels, i.e. 'person'
+show_conf: True # (bool) show prediction confidence, i.e. '0.99'
+show_boxes: True # (bool) show prediction boxes
+line_width: # (int, optional) line width of the bounding boxes. Scaled to image size if None.
+
+# Export settings ------------------------------------------------------------------------------------------------------
+format: torchscript # (str) format to export to, choices at https://docs.ultralytics.com/modes/export/#export-formats
+keras: False # (bool) use Kera=s
+optimize: False # (bool) TorchScript: optimize for mobile
+int8: False # (bool) CoreML/TF INT8 quantization
+dynamic: False # (bool) ONNX/TF/TensorRT: dynamic axes
+simplify: True # (bool) ONNX: simplify model using `onnxslim`
+opset: # (int, optional) ONNX: opset version
+workspace: None # (float, optional) TensorRT: workspace size (GiB), `None` will let TensorRT auto-allocate memory
+nms: False # (bool) CoreML: add NMS
+
+# Hyperparameters ------------------------------------------------------------------------------------------------------
+lr0: 0.01 # (float) initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
+lrf: 0.01 # (float) final learning rate (lr0 * lrf)
+momentum: 0.937 # (float) SGD momentum/Adam beta1
+weight_decay: 0.0005 # (float) optimizer weight decay 5e-4
+warmup_epochs: 3.0 # (float) warmup epochs (fractions ok)
+warmup_momentum: 0.8 # (float) warmup initial momentum
+warmup_bias_lr: 0.0 # 0.1 # (float) warmup initial bias lr
+box: 7.5 # (float) box loss gain
+cls: 0.5 # (float) cls loss gain (scale with pixels)
+dfl: 1.5 # (float) dfl loss gain
+pose: 12.0 # (float) pose loss gain
+kobj: 1.0 # (float) keypoint obj loss gain
+nbs: 64 # (int) nominal batch size
+hsv_h: 0.015 # (float) image HSV-Hue augmentation (fraction)
+hsv_s: 0.7 # (float) image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4 # (float) image HSV-Value augmentation (fraction)
+degrees: 0.0 # (float) image rotation (+/- deg)
+translate: 0.1 # (float) image translation (+/- fraction)
+scale: 0.5 # (float) image scale (+/- gain)
+shear: 0.0 # (float) image shear (+/- deg)
+perspective: 0.0 # (float) image perspective (+/- fraction), range 0-0.001
+flipud: 0.0 # (float) image flip up-down (probability)
+fliplr: 0.5 # (float) image flip left-right (probability)
+bgr: 0.0 # (float) image channel BGR (probability)
+
+mosaic: 1.0 # (float) image mosaic (probability)
+mixup: 0.0 # (float) image mixup (probability)
+copy_paste: 0.1 # (float) segment copy-paste (probability)
+
+copy_paste_mode: "flip" # (str) the method to do copy_paste augmentation (flip, mixup)
+auto_augment: randaugment # (str) auto augmentation policy for classification (randaugment, autoaugment, augmix)
+erasing: 0.4 # (float) probability of random erasing during classification training (0-0.9), 0 means no erasing, must be less than 1.0.
+crop_fraction: 1.0 # (float) image crop fraction for classification (0.1-1), 1.0 means no crop, must be greater than 0.
+
+# Custom config.yaml ---------------------------------------------------------------------------------------------------
+cfg: # (str, optional) for overriding defaults.yaml
+
+# Tracker settings ------------------------------------------------------------------------------------------------------
+tracker: botsort.yaml # (str) tracker type, choices=[botsort.yaml, bytetrack.yaml]
diff --git a/ultralytics/cfg/models/11/yolo11-cls-resnet18.yaml b/ultralytics/cfg/models/11/yolo11-cls-resnet18.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..baedcb5dc5abcbf02bb58ff2f512e11cf5dbc2c8
--- /dev/null
+++ b/ultralytics/cfg/models/11/yolo11-cls-resnet18.yaml
@@ -0,0 +1,24 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO11-cls image classification model with ResNet18 backbone
+# Model docs: https://docs.ultralytics.com/models/yolo11
+# Task docs: https://docs.ultralytics.com/tasks/classify
+
+# Parameters
+nc: 10 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolo11n-cls.yaml' will call yolo11-cls.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024]
+ s: [0.33, 0.50, 1024]
+ m: [0.67, 0.75, 1024]
+ l: [1.00, 1.00, 1024]
+ x: [1.00, 1.25, 1024]
+
+# ResNet18 backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, TorchVision, [512, "resnet18", "DEFAULT", True, 2]] # truncate two layers from the end
+
+# YOLO11n head
+head:
+ - [-1, 1, Classify, [nc]] # Classify
diff --git a/ultralytics/cfg/models/11/yolo11-cls.yaml b/ultralytics/cfg/models/11/yolo11-cls.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7a6457c6d6d9728878aa9a4b8a4d76708f39e0e0
--- /dev/null
+++ b/ultralytics/cfg/models/11/yolo11-cls.yaml
@@ -0,0 +1,33 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO11-cls image classification model
+# Model docs: https://docs.ultralytics.com/models/yolo11
+# Task docs: https://docs.ultralytics.com/tasks/classify
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolo11n-cls.yaml' will call yolo11-cls.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.50, 0.25, 1024] # summary: 151 layers, 1633584 parameters, 1633584 gradients, 3.3 GFLOPs
+ s: [0.50, 0.50, 1024] # summary: 151 layers, 5545488 parameters, 5545488 gradients, 12.2 GFLOPs
+ m: [0.50, 1.00, 512] # summary: 187 layers, 10455696 parameters, 10455696 gradients, 39.7 GFLOPs
+ l: [1.00, 1.00, 512] # summary: 309 layers, 12937104 parameters, 12937104 gradients, 49.9 GFLOPs
+ x: [1.00, 1.50, 512] # summary: 309 layers, 28458544 parameters, 28458544 gradients, 111.1 GFLOPs
+
+# YOLO11n backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 2, C3k2, [256, False, 0.25]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 2, C3k2, [512, False, 0.25]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 2, C3k2, [512, True]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+ - [-1, 2, C3k2, [1024, True]]
+ - [-1, 2, C2PSA, [1024]] # 9
+
+# YOLO11n head
+head:
+ - [-1, 1, Classify, [nc]] # Classify
diff --git a/ultralytics/cfg/models/11/yolo11-obb.yaml b/ultralytics/cfg/models/11/yolo11-obb.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8625c7cfdace7d120134711627fc86c7468be714
--- /dev/null
+++ b/ultralytics/cfg/models/11/yolo11-obb.yaml
@@ -0,0 +1,50 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO11-obb Oriented Bounding Boxes (OBB) model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo11
+# Task docs: https://docs.ultralytics.com/tasks/obb
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolo11n-obb.yaml' will call yolo11-obb.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.50, 0.25, 1024] # summary: 344 layers, 2695747 parameters, 2695731 gradients, 6.9 GFLOPs
+ s: [0.50, 0.50, 1024] # summary: 344 layers, 9744931 parameters, 9744915 gradients, 22.7 GFLOPs
+ m: [0.50, 1.00, 512] # summary: 434 layers, 20963523 parameters, 20963507 gradients, 72.2 GFLOPs
+ l: [1.00, 1.00, 512] # summary: 656 layers, 26220995 parameters, 26220979 gradients, 91.3 GFLOPs
+ x: [1.00, 1.50, 512] # summary: 656 layers, 58875331 parameters, 58875315 gradients, 204.3 GFLOPs
+
+# YOLO11n backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 2, C3k2, [256, False, 0.25]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 2, C3k2, [512, False, 0.25]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 2, C3k2, [512, True]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+ - [-1, 2, C3k2, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+ - [-1, 2, C2PSA, [1024]] # 10
+
+# YOLO11n head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 2, C3k2, [512, False]] # 13
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 13], 1, Concat, [1]] # cat head P4
+ - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
+
+ - [-1, 1, Conv, [512, 3, 2]]
+ - [[-1, 10], 1, Concat, [1]] # cat head P5
+ - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
+
+ - [[16, 19, 22], 1, OBB, [nc, 1]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/11/yolo11-pose.yaml b/ultralytics/cfg/models/11/yolo11-pose.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7470edac2fa377ad5913911f2607c47e2cc1eba9
--- /dev/null
+++ b/ultralytics/cfg/models/11/yolo11-pose.yaml
@@ -0,0 +1,51 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO11-pose keypoints/pose estimation model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo11
+# Task docs: https://docs.ultralytics.com/tasks/pose
+
+# Parameters
+nc: 80 # number of classes
+kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+scales: # model compound scaling constants, i.e. 'model=yolo11n-pose.yaml' will call yolo11.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.50, 0.25, 1024] # summary: 344 layers, 2908507 parameters, 2908491 gradients, 7.7 GFLOPs
+ s: [0.50, 0.50, 1024] # summary: 344 layers, 9948811 parameters, 9948795 gradients, 23.5 GFLOPs
+ m: [0.50, 1.00, 512] # summary: 434 layers, 20973273 parameters, 20973257 gradients, 72.3 GFLOPs
+ l: [1.00, 1.00, 512] # summary: 656 layers, 26230745 parameters, 26230729 gradients, 91.4 GFLOPs
+ x: [1.00, 1.50, 512] # summary: 656 layers, 58889881 parameters, 58889865 gradients, 204.3 GFLOPs
+
+# YOLO11n backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 2, C3k2, [256, False, 0.25]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 2, C3k2, [512, False, 0.25]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 2, C3k2, [512, True]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+ - [-1, 2, C3k2, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+ - [-1, 2, C2PSA, [1024]] # 10
+
+# YOLO11n head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 2, C3k2, [512, False]] # 13
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 13], 1, Concat, [1]] # cat head P4
+ - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
+
+ - [-1, 1, Conv, [512, 3, 2]]
+ - [[-1, 10], 1, Concat, [1]] # cat head P5
+ - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
+
+ - [[16, 19, 22], 1, Pose, [nc, kpt_shape]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/11/yolo11-seg.yaml b/ultralytics/cfg/models/11/yolo11-seg.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a569f4af84dfdd698f90438c67a3b121f777c143
--- /dev/null
+++ b/ultralytics/cfg/models/11/yolo11-seg.yaml
@@ -0,0 +1,50 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO11-seg instance segmentation model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo11
+# Task docs: https://docs.ultralytics.com/tasks/segment
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolo11n-seg.yaml' will call yolo11-seg.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.50, 0.25, 1024] # summary: 355 layers, 2876848 parameters, 2876832 gradients, 10.5 GFLOPs
+ s: [0.50, 0.50, 1024] # summary: 355 layers, 10113248 parameters, 10113232 gradients, 35.8 GFLOPs
+ m: [0.50, 1.00, 512] # summary: 445 layers, 22420896 parameters, 22420880 gradients, 123.9 GFLOPs
+ l: [1.00, 1.00, 512] # summary: 667 layers, 27678368 parameters, 27678352 gradients, 143.0 GFLOPs
+ x: [1.00, 1.50, 512] # summary: 667 layers, 62142656 parameters, 62142640 gradients, 320.2 GFLOPs
+
+# YOLO11n backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 2, C3k2, [256, False, 0.25]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 2, C3k2, [512, False, 0.25]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 2, C3k2, [512, True]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+ - [-1, 2, C3k2, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+ - [-1, 2, C2PSA, [1024]] # 10
+
+# YOLO11n head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 2, C3k2, [512, False]] # 13
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 13], 1, Concat, [1]] # cat head P4
+ - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
+
+ - [-1, 1, Conv, [512, 3, 2]]
+ - [[-1, 10], 1, Concat, [1]] # cat head P5
+ - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
+
+ - [[16, 19, 22], 1, Segment, [nc, 32, 256]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/11/yolo11.yaml b/ultralytics/cfg/models/11/yolo11.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..409465a1bb78944a9866505e5f0c2602ef76c99b
--- /dev/null
+++ b/ultralytics/cfg/models/11/yolo11.yaml
@@ -0,0 +1,50 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLO11 object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo11
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolo11n.yaml' will call yolo11.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.50, 0.25, 1024] # summary: 319 layers, 2624080 parameters, 2624064 gradients, 6.6 GFLOPs
+ s: [0.50, 0.50, 1024] # summary: 319 layers, 9458752 parameters, 9458736 gradients, 21.7 GFLOPs
+ m: [0.50, 1.00, 512] # summary: 409 layers, 20114688 parameters, 20114672 gradients, 68.5 GFLOPs
+ l: [1.00, 1.00, 512] # summary: 631 layers, 25372160 parameters, 25372144 gradients, 87.6 GFLOPs
+ x: [1.00, 1.50, 512] # summary: 631 layers, 56966176 parameters, 56966160 gradients, 196.0 GFLOPs
+
+# YOLO11n backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 2, C3k2, [256, False, 0.25]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 2, C3k2, [512, False, 0.25]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 2, C3k2, [512, True]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+ - [-1, 2, C3k2, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+ - [-1, 2, C2PSA, [1024]] # 10
+
+# YOLO11n head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 2, C3k2, [512, False]] # 13
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 2, C3k2, [256, False]] # 16 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 13], 1, Concat, [1]] # cat head P4
+ - [-1, 2, C3k2, [512, False]] # 19 (P4/16-medium)
+
+ - [-1, 1, Conv, [512, 3, 2]]
+ - [[-1, 10], 1, Concat, [1]] # cat head P5
+ - [-1, 2, C3k2, [1024, True]] # 22 (P5/32-large)
+
+ - [[16, 19, 22], 1, Detect, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/README.md b/ultralytics/cfg/models/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..68a9238384ec4c8f08c1db1fd6bad95e824d96d4
--- /dev/null
+++ b/ultralytics/cfg/models/README.md
@@ -0,0 +1,48 @@
+## Models
+
+Welcome to the [Ultralytics](https://www.ultralytics.com/) Models directory! Here you will find a wide variety of pre-configured model configuration files (`*.yaml`s) that can be used to create custom YOLO models. The models in this directory have been expertly crafted and fine-tuned by the Ultralytics team to provide the best performance for a wide range of object detection and image segmentation tasks.
+
+These model configurations cover a wide range of scenarios, from simple object detection to more complex tasks like instance segmentation and object tracking. They are also designed to run efficiently on a variety of hardware platforms, from CPUs to GPUs. Whether you are a seasoned machine learning practitioner or just getting started with YOLO, this directory provides a great starting point for your custom model development needs.
+
+To get started, simply browse through the models in this directory and find one that best suits your needs. Once you've selected a model, you can use the provided `*.yaml` file to train and deploy your custom YOLO model with ease. See full details at the Ultralytics [Docs](https://docs.ultralytics.com/models/), and if you need help or have any questions, feel free to reach out to the Ultralytics team for support. So, don't wait, start creating your custom YOLO model now!
+
+### Usage
+
+Model `*.yaml` files may be used directly in the [Command Line Interface (CLI)](https://docs.ultralytics.com/usage/cli/) with a `yolo` command:
+
+```bash
+# Train a YOLO11n model using the coco8 dataset for 100 epochs
+yolo task=detect mode=train model=yolo11n.yaml data=coco8.yaml epochs=100
+```
+
+They may also be used directly in a Python environment, and accept the same [arguments](https://docs.ultralytics.com/usage/cfg/) as in the CLI example above:
+
+```python
+from ultralytics import YOLO
+
+# Initialize a YOLO11n model from a YAML configuration file
+model = YOLO("model.yaml")
+
+# If a pre-trained model is available, use it instead
+# model = YOLO("model.pt")
+
+# Display model information
+model.info()
+
+# Train the model using the COCO8 dataset for 100 epochs
+model.train(data="coco8.yaml", epochs=100)
+```
+
+## Pre-trained Model Architectures
+
+Ultralytics supports many model architectures. Visit [Ultralytics Models](https://docs.ultralytics.com/models/) to view detailed information and usage. Any of these models can be used by loading their configurations or pretrained checkpoints if available.
+
+## Contribute New Models
+
+Have you trained a new YOLO variant or achieved state-of-the-art performance with specific tuning? We'd love to showcase your work in our Models section! Contributions from the community in the form of new models, architectures, or optimizations are highly valued and can significantly enrich our repository.
+
+By contributing to this section, you're helping us offer a wider array of model choices and configurations to the community. It's a fantastic way to share your knowledge and expertise while making the Ultralytics YOLO ecosystem even more versatile.
+
+To get started, please consult our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) for step-by-step instructions on how to submit a Pull Request (PR) 🛠️. Your contributions are eagerly awaited!
+
+Let's join hands to extend the range and capabilities of the Ultralytics YOLO models 🙏!
diff --git a/ultralytics/cfg/models/rt-detr/rtdetr-l.yaml b/ultralytics/cfg/models/rt-detr/rtdetr-l.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d8d6b4f410be5cfdea18c1d3dae48501e443fec2
--- /dev/null
+++ b/ultralytics/cfg/models/rt-detr/rtdetr-l.yaml
@@ -0,0 +1,53 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics RT-DETR-l hybrid object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/rtdetr
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
+ # [depth, width, max_channels]
+ l: [1.00, 1.00, 1024]
+
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, HGStem, [32, 48]] # 0-P2/4
+ - [-1, 6, HGBlock, [48, 128, 3]] # stage 1
+
+ - [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8
+ - [-1, 6, HGBlock, [96, 512, 3]] # stage 2
+
+ - [-1, 1, DWConv, [512, 3, 2, 1, False]] # 4-P3/16
+ - [-1, 6, HGBlock, [192, 1024, 5, True, False]] # cm, c2, k, light, shortcut
+ - [-1, 6, HGBlock, [192, 1024, 5, True, True]]
+ - [-1, 6, HGBlock, [192, 1024, 5, True, True]] # stage 3
+
+ - [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 8-P4/32
+ - [-1, 6, HGBlock, [384, 2048, 5, True, False]] # stage 4
+
+head:
+ - [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 10 input_proj.2
+ - [-1, 1, AIFI, [1024, 8]]
+ - [-1, 1, Conv, [256, 1, 1]] # 12, Y5, lateral_convs.0
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [7, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14 input_proj.1
+ - [[-2, -1], 1, Concat, [1]]
+ - [-1, 3, RepC3, [256]] # 16, fpn_blocks.0
+ - [-1, 1, Conv, [256, 1, 1]] # 17, Y4, lateral_convs.1
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 19 input_proj.0
+ - [[-2, -1], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, RepC3, [256]] # X3 (21), fpn_blocks.1
+
+ - [-1, 1, Conv, [256, 3, 2]] # 22, downsample_convs.0
+ - [[-1, 17], 1, Concat, [1]] # cat Y4
+ - [-1, 3, RepC3, [256]] # F4 (24), pan_blocks.0
+
+ - [-1, 1, Conv, [256, 3, 2]] # 25, downsample_convs.1
+ - [[-1, 12], 1, Concat, [1]] # cat Y5
+ - [-1, 3, RepC3, [256]] # F5 (27), pan_blocks.1
+
+ - [[21, 24, 27], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/rt-detr/rtdetr-resnet101.yaml b/ultralytics/cfg/models/rt-detr/rtdetr-resnet101.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b13e94512bd5f995937a995002d66c93bff7803f
--- /dev/null
+++ b/ultralytics/cfg/models/rt-detr/rtdetr-resnet101.yaml
@@ -0,0 +1,45 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics RT-DETR-ResNet101 hybrid object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/rtdetr
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
+ # [depth, width, max_channels]
+ l: [1.00, 1.00, 1024]
+
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0
+ - [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1
+ - [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2
+ - [-1, 1, ResNetLayer, [512, 256, 2, False, 23]] # 3
+ - [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4
+
+head:
+ - [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 5
+ - [-1, 1, AIFI, [1024, 8]]
+ - [-1, 1, Conv, [256, 1, 1]] # 7
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 9
+ - [[-2, -1], 1, Concat, [1]]
+ - [-1, 3, RepC3, [256]] # 11
+ - [-1, 1, Conv, [256, 1, 1]] # 12
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [2, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14
+ - [[-2, -1], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, RepC3, [256]] # X3 (16), fpn_blocks.1
+
+ - [-1, 1, Conv, [256, 3, 2]] # 17, downsample_convs.0
+ - [[-1, 12], 1, Concat, [1]] # cat Y4
+ - [-1, 3, RepC3, [256]] # F4 (19), pan_blocks.0
+
+ - [-1, 1, Conv, [256, 3, 2]] # 20, downsample_convs.1
+ - [[-1, 7], 1, Concat, [1]] # cat Y5
+ - [-1, 3, RepC3, [256]] # F5 (22), pan_blocks.1
+
+ - [[16, 19, 22], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/rt-detr/rtdetr-resnet50.yaml b/ultralytics/cfg/models/rt-detr/rtdetr-resnet50.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..8172ad4ed4c4fe9263b87d2595a61625d0644ad2
--- /dev/null
+++ b/ultralytics/cfg/models/rt-detr/rtdetr-resnet50.yaml
@@ -0,0 +1,45 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics RT-DETR-ResNet50 hybrid object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/rtdetr
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
+ # [depth, width, max_channels]
+ l: [1.00, 1.00, 1024]
+
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0
+ - [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1
+ - [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2
+ - [-1, 1, ResNetLayer, [512, 256, 2, False, 6]] # 3
+ - [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4
+
+head:
+ - [-1, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 5
+ - [-1, 1, AIFI, [1024, 8]]
+ - [-1, 1, Conv, [256, 1, 1]] # 7
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [3, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 9
+ - [[-2, -1], 1, Concat, [1]]
+ - [-1, 3, RepC3, [256]] # 11
+ - [-1, 1, Conv, [256, 1, 1]] # 12
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [2, 1, Conv, [256, 1, 1, None, 1, 1, False]] # 14
+ - [[-2, -1], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, RepC3, [256]] # X3 (16), fpn_blocks.1
+
+ - [-1, 1, Conv, [256, 3, 2]] # 17, downsample_convs.0
+ - [[-1, 12], 1, Concat, [1]] # cat Y4
+ - [-1, 3, RepC3, [256]] # F4 (19), pan_blocks.0
+
+ - [-1, 1, Conv, [256, 3, 2]] # 20, downsample_convs.1
+ - [[-1, 7], 1, Concat, [1]] # cat Y5
+ - [-1, 3, RepC3, [256]] # F5 (22), pan_blocks.1
+
+ - [[16, 19, 22], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/rt-detr/rtdetr-x.yaml b/ultralytics/cfg/models/rt-detr/rtdetr-x.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f9c4a19c8ab919d06a68ff655d6ee788ac5b23a0
--- /dev/null
+++ b/ultralytics/cfg/models/rt-detr/rtdetr-x.yaml
@@ -0,0 +1,57 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics RT-DETR-x hybrid object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/rtdetr
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
+ # [depth, width, max_channels]
+ x: [1.00, 1.00, 2048]
+
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, HGStem, [32, 64]] # 0-P2/4
+ - [-1, 6, HGBlock, [64, 128, 3]] # stage 1
+
+ - [-1, 1, DWConv, [128, 3, 2, 1, False]] # 2-P3/8
+ - [-1, 6, HGBlock, [128, 512, 3]]
+ - [-1, 6, HGBlock, [128, 512, 3, False, True]] # 4-stage 2
+
+ - [-1, 1, DWConv, [512, 3, 2, 1, False]] # 5-P3/16
+ - [-1, 6, HGBlock, [256, 1024, 5, True, False]] # cm, c2, k, light, shortcut
+ - [-1, 6, HGBlock, [256, 1024, 5, True, True]]
+ - [-1, 6, HGBlock, [256, 1024, 5, True, True]]
+ - [-1, 6, HGBlock, [256, 1024, 5, True, True]]
+ - [-1, 6, HGBlock, [256, 1024, 5, True, True]] # 10-stage 3
+
+ - [-1, 1, DWConv, [1024, 3, 2, 1, False]] # 11-P4/32
+ - [-1, 6, HGBlock, [512, 2048, 5, True, False]]
+ - [-1, 6, HGBlock, [512, 2048, 5, True, True]] # 13-stage 4
+
+head:
+ - [-1, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 14 input_proj.2
+ - [-1, 1, AIFI, [2048, 8]]
+ - [-1, 1, Conv, [384, 1, 1]] # 16, Y5, lateral_convs.0
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [10, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 18 input_proj.1
+ - [[-2, -1], 1, Concat, [1]]
+ - [-1, 3, RepC3, [384]] # 20, fpn_blocks.0
+ - [-1, 1, Conv, [384, 1, 1]] # 21, Y4, lateral_convs.1
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [4, 1, Conv, [384, 1, 1, None, 1, 1, False]] # 23 input_proj.0
+ - [[-2, -1], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, RepC3, [384]] # X3 (25), fpn_blocks.1
+
+ - [-1, 1, Conv, [384, 3, 2]] # 26, downsample_convs.0
+ - [[-1, 21], 1, Concat, [1]] # cat Y4
+ - [-1, 3, RepC3, [384]] # F4 (28), pan_blocks.0
+
+ - [-1, 1, Conv, [384, 3, 2]] # 29, downsample_convs.1
+ - [[-1, 16], 1, Concat, [1]] # cat Y5
+ - [-1, 3, RepC3, [384]] # F5 (31), pan_blocks.1
+
+ - [[25, 28, 31], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v10/yolov10b.yaml b/ultralytics/cfg/models/v10/yolov10b.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..750379128cc77d22329cbe9315ce5a644b722baa
--- /dev/null
+++ b/ultralytics/cfg/models/v10/yolov10b.yaml
@@ -0,0 +1,45 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLOv10b object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov10
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov10n.yaml' will call yolov10.yaml with scale 'n'
+ # [depth, width, max_channels]
+ b: [0.67, 1.00, 512]
+
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C2f, [128, True]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C2f, [256, True]]
+ - [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C2f, [512, True]]
+ - [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
+ - [-1, 3, C2fCIB, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+ - [-1, 1, PSA, [1024]] # 10
+
+# YOLOv10.0n head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C2fCIB, [512, True]] # 13
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C2f, [256]] # 16 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 13], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
+
+ - [-1, 1, SCDown, [512, 3, 2]]
+ - [[-1, 10], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
+
+ - [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v10/yolov10l.yaml b/ultralytics/cfg/models/v10/yolov10l.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1dedd752e2372c186ffef0e10e8272227af1ce27
--- /dev/null
+++ b/ultralytics/cfg/models/v10/yolov10l.yaml
@@ -0,0 +1,45 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLOv10l object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov10
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov10n.yaml' will call yolov10.yaml with scale 'n'
+ # [depth, width, max_channels]
+ l: [1.00, 1.00, 512]
+
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C2f, [128, True]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C2f, [256, True]]
+ - [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C2f, [512, True]]
+ - [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
+ - [-1, 3, C2fCIB, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+ - [-1, 1, PSA, [1024]] # 10
+
+# YOLOv10.0n head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C2fCIB, [512, True]] # 13
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C2f, [256]] # 16 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 13], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
+
+ - [-1, 1, SCDown, [512, 3, 2]]
+ - [[-1, 10], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
+
+ - [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v10/yolov10m.yaml b/ultralytics/cfg/models/v10/yolov10m.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6ba4020b3309322ca22dd84d107f3dd75802d5fa
--- /dev/null
+++ b/ultralytics/cfg/models/v10/yolov10m.yaml
@@ -0,0 +1,45 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLOv10m object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov10
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov10n.yaml' will call yolov10.yaml with scale 'n'
+ # [depth, width, max_channels]
+ m: [0.67, 0.75, 768]
+
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C2f, [128, True]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C2f, [256, True]]
+ - [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C2f, [512, True]]
+ - [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
+ - [-1, 3, C2fCIB, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+ - [-1, 1, PSA, [1024]] # 10
+
+# YOLOv10.0n head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C2f, [512]] # 13
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C2f, [256]] # 16 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 13], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
+
+ - [-1, 1, SCDown, [512, 3, 2]]
+ - [[-1, 10], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
+
+ - [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v10/yolov10n.yaml b/ultralytics/cfg/models/v10/yolov10n.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a9aa7018950c2897c6efb3f38ad780016db8817b
--- /dev/null
+++ b/ultralytics/cfg/models/v10/yolov10n.yaml
@@ -0,0 +1,45 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLOv10n object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov10
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov10n.yaml' will call yolov10.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024]
+
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C2f, [128, True]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C2f, [256, True]]
+ - [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C2f, [512, True]]
+ - [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
+ - [-1, 3, C2f, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+ - [-1, 1, PSA, [1024]] # 10
+
+# YOLOv10.0n head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C2f, [512]] # 13
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C2f, [256]] # 16 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 13], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C2f, [512]] # 19 (P4/16-medium)
+
+ - [-1, 1, SCDown, [512, 3, 2]]
+ - [[-1, 10], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C2fCIB, [1024, True, True]] # 22 (P5/32-large)
+
+ - [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v10/yolov10s.yaml b/ultralytics/cfg/models/v10/yolov10s.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..dbb678b277d72bd2aabca2473974193495157559
--- /dev/null
+++ b/ultralytics/cfg/models/v10/yolov10s.yaml
@@ -0,0 +1,45 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLOv10s object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov10
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov10n.yaml' will call yolov10.yaml with scale 'n'
+ # [depth, width, max_channels]
+ s: [0.33, 0.50, 1024]
+
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C2f, [128, True]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C2f, [256, True]]
+ - [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C2f, [512, True]]
+ - [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
+ - [-1, 3, C2fCIB, [1024, True, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+ - [-1, 1, PSA, [1024]] # 10
+
+# YOLOv10.0n head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C2f, [512]] # 13
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C2f, [256]] # 16 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 13], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C2f, [512]] # 19 (P4/16-medium)
+
+ - [-1, 1, SCDown, [512, 3, 2]]
+ - [[-1, 10], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C2fCIB, [1024, True, True]] # 22 (P5/32-large)
+
+ - [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v10/yolov10x.yaml b/ultralytics/cfg/models/v10/yolov10x.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..57482133863ee7eee26b66f83d2d6567c9fa9baf
--- /dev/null
+++ b/ultralytics/cfg/models/v10/yolov10x.yaml
@@ -0,0 +1,45 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLOv10x object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov10
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov10n.yaml' will call yolov10.yaml with scale 'n'
+ # [depth, width, max_channels]
+ x: [1.00, 1.25, 512]
+
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C2f, [128, True]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C2f, [256, True]]
+ - [-1, 1, SCDown, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C2fCIB, [512, True]]
+ - [-1, 1, SCDown, [1024, 3, 2]] # 7-P5/32
+ - [-1, 3, C2fCIB, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+ - [-1, 1, PSA, [1024]] # 10
+
+# YOLOv10.0n head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C2fCIB, [512, True]] # 13
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C2f, [256]] # 16 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 13], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C2fCIB, [512, True]] # 19 (P4/16-medium)
+
+ - [-1, 1, SCDown, [512, 3, 2]]
+ - [[-1, 10], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C2fCIB, [1024, True]] # 22 (P5/32-large)
+
+ - [[16, 19, 22], 1, v10Detect, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v12/yolov12.yaml b/ultralytics/cfg/models/v12/yolov12.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..dca26329faf534768a8b1589bf0339d063caa532
--- /dev/null
+++ b/ultralytics/cfg/models/v12/yolov12.yaml
@@ -0,0 +1,45 @@
+# YOLOv12 🚀, AGPL-3.0 license
+# YOLOv12 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov12n.yaml' will call yolov12.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.50, 0.25, 1024] # summary: 465 layers, 2,603,056 parameters, 2,603,040 gradients, 6.7 GFLOPs
+ s: [0.50, 0.50, 1024] # summary: 465 layers, 9,285,632 parameters, 9,285,616 gradients, 21.7 GFLOPs
+ m: [0.50, 1.00, 512] # summary: 501 layers, 20,201,216 parameters, 20,201,200 gradients, 68.1 GFLOPs
+ l: [1.00, 1.00, 512] # summary: 831 layers, 26,454,880 parameters, 26,454,864 gradients, 89.7 GFLOPs
+ x: [1.00, 1.50, 512] # summary: 831 layers, 59,216,928 parameters, 59,216,912 gradients, 200.3 GFLOPs
+
+# YOLO12n backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 2, C3k2, [256, False, 0.25]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 2, C3k2, [512, False, 0.25]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 4, A2C2f, [512, True, 4]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+ - [-1, 4, A2C2f, [1024, True, 1]] # 8
+
+# YOLO12n head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 2, A2C2f, [512, False, -1]] # 11
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 2, A2C2f, [256, False, -1]] # 14
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 11], 1, Concat, [1]] # cat head P4
+ - [-1, 2, A2C2f, [512, False, -1]] # 17
+
+ - [-1, 1, Conv, [512, 3, 2]]
+ - [[-1, 8], 1, Concat, [1]] # cat head P5
+ - [-1, 2, C3k2, [1024, True]] # 20 (P5/32-large)
+
+ - [[14, 17, 20], 1, Detect, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v3/yolov3-spp.yaml b/ultralytics/cfg/models/v3/yolov3-spp.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6aef25ab748bc84ee6ad054057029de97e7e1b14
--- /dev/null
+++ b/ultralytics/cfg/models/v3/yolov3-spp.yaml
@@ -0,0 +1,49 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv3-SPP object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov3
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+
+# darknet53 backbone
+backbone:
+ # [from, number, module, args]
+ - [-1, 1, Conv, [32, 3, 1]] # 0
+ - [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
+ - [-1, 1, Bottleneck, [64]]
+ - [-1, 1, Conv, [128, 3, 2]] # 3-P2/4
+ - [-1, 2, Bottleneck, [128]]
+ - [-1, 1, Conv, [256, 3, 2]] # 5-P3/8
+ - [-1, 8, Bottleneck, [256]]
+ - [-1, 1, Conv, [512, 3, 2]] # 7-P4/16
+ - [-1, 8, Bottleneck, [512]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 9-P5/32
+ - [-1, 4, Bottleneck, [1024]] # 10
+
+# YOLOv3-SPP head
+head:
+ - [-1, 1, Bottleneck, [1024, False]]
+ - [-1, 1, SPP, [512, [5, 9, 13]]]
+ - [-1, 1, Conv, [1024, 3, 1]]
+ - [-1, 1, Conv, [512, 1, 1]]
+ - [-1, 1, Conv, [1024, 3, 1]] # 15 (P5/32-large)
+
+ - [-2, 1, Conv, [256, 1, 1]]
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 8], 1, Concat, [1]] # cat backbone P4
+ - [-1, 1, Bottleneck, [512, False]]
+ - [-1, 1, Bottleneck, [512, False]]
+ - [-1, 1, Conv, [256, 1, 1]]
+ - [-1, 1, Conv, [512, 3, 1]] # 22 (P4/16-medium)
+
+ - [-2, 1, Conv, [128, 1, 1]]
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P3
+ - [-1, 1, Bottleneck, [256, False]]
+ - [-1, 2, Bottleneck, [256, False]] # 27 (P3/8-small)
+
+ - [[27, 22, 15], 1, Detect, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v3/yolov3-tiny.yaml b/ultralytics/cfg/models/v3/yolov3-tiny.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..91a0bb03f7d8f8436cd4a09cb8e46d8e38484c5c
--- /dev/null
+++ b/ultralytics/cfg/models/v3/yolov3-tiny.yaml
@@ -0,0 +1,40 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv3-tiiny object detection model with P4/16 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov3
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+
+# YOLOv3-tiny backbone
+backbone:
+ # [from, number, module, args]
+ - [-1, 1, Conv, [16, 3, 1]] # 0
+ - [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 1-P1/2
+ - [-1, 1, Conv, [32, 3, 1]]
+ - [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 3-P2/4
+ - [-1, 1, Conv, [64, 3, 1]]
+ - [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 5-P3/8
+ - [-1, 1, Conv, [128, 3, 1]]
+ - [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 7-P4/16
+ - [-1, 1, Conv, [256, 3, 1]]
+ - [-1, 1, nn.MaxPool2d, [2, 2, 0]] # 9-P5/32
+ - [-1, 1, Conv, [512, 3, 1]]
+ - [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]] # 11
+ - [-1, 1, nn.MaxPool2d, [2, 1, 0]] # 12
+
+# YOLOv3-tiny head
+head:
+ - [-1, 1, Conv, [1024, 3, 1]]
+ - [-1, 1, Conv, [256, 1, 1]]
+ - [-1, 1, Conv, [512, 3, 1]] # 15 (P5/32-large)
+
+ - [-2, 1, Conv, [128, 1, 1]]
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 8], 1, Concat, [1]] # cat backbone P4
+ - [-1, 1, Conv, [256, 3, 1]] # 19 (P4/16-medium)
+
+ - [[19, 15], 1, Detect, [nc]] # Detect(P4, P5)
diff --git a/ultralytics/cfg/models/v3/yolov3.yaml b/ultralytics/cfg/models/v3/yolov3.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..95c99de52be649df826589f73da346d4a75dd05f
--- /dev/null
+++ b/ultralytics/cfg/models/v3/yolov3.yaml
@@ -0,0 +1,49 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv3 object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov3
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+depth_multiple: 1.0 # model depth multiple
+width_multiple: 1.0 # layer channel multiple
+
+# darknet53 backbone
+backbone:
+ # [from, number, module, args]
+ - [-1, 1, Conv, [32, 3, 1]] # 0
+ - [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
+ - [-1, 1, Bottleneck, [64]]
+ - [-1, 1, Conv, [128, 3, 2]] # 3-P2/4
+ - [-1, 2, Bottleneck, [128]]
+ - [-1, 1, Conv, [256, 3, 2]] # 5-P3/8
+ - [-1, 8, Bottleneck, [256]]
+ - [-1, 1, Conv, [512, 3, 2]] # 7-P4/16
+ - [-1, 8, Bottleneck, [512]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 9-P5/32
+ - [-1, 4, Bottleneck, [1024]] # 10
+
+# YOLOv3 head
+head:
+ - [-1, 1, Bottleneck, [1024, False]]
+ - [-1, 1, Conv, [512, 1, 1]]
+ - [-1, 1, Conv, [1024, 3, 1]]
+ - [-1, 1, Conv, [512, 1, 1]]
+ - [-1, 1, Conv, [1024, 3, 1]] # 15 (P5/32-large)
+
+ - [-2, 1, Conv, [256, 1, 1]]
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 8], 1, Concat, [1]] # cat backbone P4
+ - [-1, 1, Bottleneck, [512, False]]
+ - [-1, 1, Bottleneck, [512, False]]
+ - [-1, 1, Conv, [256, 1, 1]]
+ - [-1, 1, Conv, [512, 3, 1]] # 22 (P4/16-medium)
+
+ - [-2, 1, Conv, [128, 1, 1]]
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P3
+ - [-1, 1, Bottleneck, [256, False]]
+ - [-1, 2, Bottleneck, [256, False]] # 27 (P3/8-small)
+
+ - [[27, 22, 15], 1, Detect, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v5/yolov5-p6.yaml b/ultralytics/cfg/models/v5/yolov5-p6.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..376d1aba90c24c0df93c14af4a45f5b98b6dbf02
--- /dev/null
+++ b/ultralytics/cfg/models/v5/yolov5-p6.yaml
@@ -0,0 +1,62 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv5 object detection model with P3/8 - P6/64 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov5
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov5n-p6.yaml' will call yolov5-p6.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024]
+ s: [0.33, 0.50, 1024]
+ m: [0.67, 0.75, 1024]
+ l: [1.00, 1.00, 1024]
+ x: [1.33, 1.25, 1024]
+
+# YOLOv5 v6.0 backbone
+backbone:
+ # [from, number, module, args]
+ - [-1, 1, Conv, [64, 6, 2, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C3, [128]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C3, [256]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 9, C3, [512]]
+ - [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
+ - [-1, 3, C3, [768]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
+ - [-1, 3, C3, [1024]]
+ - [-1, 1, SPPF, [1024, 5]] # 11
+
+# YOLOv5 v6.0 head
+head:
+ - [-1, 1, Conv, [768, 1, 1]]
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 8], 1, Concat, [1]] # cat backbone P5
+ - [-1, 3, C3, [768, False]] # 15
+
+ - [-1, 1, Conv, [512, 1, 1]]
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C3, [512, False]] # 19
+
+ - [-1, 1, Conv, [256, 1, 1]]
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C3, [256, False]] # 23 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 20], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C3, [512, False]] # 26 (P4/16-medium)
+
+ - [-1, 1, Conv, [512, 3, 2]]
+ - [[-1, 16], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C3, [768, False]] # 29 (P5/32-large)
+
+ - [-1, 1, Conv, [768, 3, 2]]
+ - [[-1, 12], 1, Concat, [1]] # cat head P6
+ - [-1, 3, C3, [1024, False]] # 32 (P6/64-xlarge)
+
+ - [[23, 26, 29, 32], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)
diff --git a/ultralytics/cfg/models/v5/yolov5.yaml b/ultralytics/cfg/models/v5/yolov5.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..76a4749ae4f102cdee4db2a14dae4912476736c4
--- /dev/null
+++ b/ultralytics/cfg/models/v5/yolov5.yaml
@@ -0,0 +1,51 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv5 object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov5
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov5n.yaml' will call yolov5.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024]
+ s: [0.33, 0.50, 1024]
+ m: [0.67, 0.75, 1024]
+ l: [1.00, 1.00, 1024]
+ x: [1.33, 1.25, 1024]
+
+# YOLOv5 v6.0 backbone
+backbone:
+ # [from, number, module, args]
+ - [-1, 1, Conv, [64, 6, 2, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C3, [128]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C3, [256]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 9, C3, [512]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+ - [-1, 3, C3, [1024]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+
+# YOLOv5 v6.0 head
+head:
+ - [-1, 1, Conv, [512, 1, 1]]
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C3, [512, False]] # 13
+
+ - [-1, 1, Conv, [256, 1, 1]]
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C3, [256, False]] # 17 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 14], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C3, [512, False]] # 20 (P4/16-medium)
+
+ - [-1, 1, Conv, [512, 3, 2]]
+ - [[-1, 10], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C3, [1024, False]] # 23 (P5/32-large)
+
+ - [[17, 20, 23], 1, Detect, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v6/yolov6.yaml b/ultralytics/cfg/models/v6/yolov6.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0812ac7e9b52bfaa2e9a54630744ffc408d71690
--- /dev/null
+++ b/ultralytics/cfg/models/v6/yolov6.yaml
@@ -0,0 +1,56 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Meituan YOLOv6 object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov6
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+activation: nn.ReLU() # (optional) model default activation function
+scales: # model compound scaling constants, i.e. 'model=yolov6n.yaml' will call yolov8.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024]
+ s: [0.33, 0.50, 1024]
+ m: [0.67, 0.75, 768]
+ l: [1.00, 1.00, 512]
+ x: [1.00, 1.25, 512]
+
+# YOLOv6-3.0s backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 6, Conv, [128, 3, 1]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 12, Conv, [256, 3, 1]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 18, Conv, [512, 3, 1]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+ - [-1, 6, Conv, [1024, 3, 1]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+
+# YOLOv6-3.0s head
+head:
+ - [-1, 1, Conv, [256, 1, 1]]
+ - [-1, 1, nn.ConvTranspose2d, [256, 2, 2, 0]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 1, Conv, [256, 3, 1]]
+ - [-1, 9, Conv, [256, 3, 1]] # 14
+
+ - [-1, 1, Conv, [128, 1, 1]]
+ - [-1, 1, nn.ConvTranspose2d, [128, 2, 2, 0]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 1, Conv, [128, 3, 1]]
+ - [-1, 9, Conv, [128, 3, 1]] # 19
+
+ - [-1, 1, Conv, [128, 3, 2]]
+ - [[-1, 15], 1, Concat, [1]] # cat head P4
+ - [-1, 1, Conv, [256, 3, 1]]
+ - [-1, 9, Conv, [256, 3, 1]] # 23
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 10], 1, Concat, [1]] # cat head P5
+ - [-1, 1, Conv, [512, 3, 1]]
+ - [-1, 9, Conv, [512, 3, 1]] # 27
+
+ - [[19, 23, 27], 1, Detect, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v8/yolov8-cls-resnet101.yaml b/ultralytics/cfg/models/v8/yolov8-cls-resnet101.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..44cc00ebf2243977441dc3e492eb860f8c442267
--- /dev/null
+++ b/ultralytics/cfg/models/v8/yolov8-cls-resnet101.yaml
@@ -0,0 +1,28 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8-cls image classification model with ResNet101 backbone
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/classify
+
+# Parameters
+nc: 1000 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024]
+ s: [0.33, 0.50, 1024]
+ m: [0.67, 0.75, 1024]
+ l: [1.00, 1.00, 1024]
+ x: [1.00, 1.25, 1024]
+
+# YOLOv8.0n backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0-P1/2
+ - [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1-P2/4
+ - [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2-P3/8
+ - [-1, 1, ResNetLayer, [512, 256, 2, False, 23]] # 3-P4/16
+ - [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4-P5/32
+
+# YOLOv8.0n head
+head:
+ - [-1, 1, Classify, [nc]] # Classify
diff --git a/ultralytics/cfg/models/v8/yolov8-cls-resnet50.yaml b/ultralytics/cfg/models/v8/yolov8-cls-resnet50.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1d05e0753fcd22195eaecd4e5dde22fb58750a14
--- /dev/null
+++ b/ultralytics/cfg/models/v8/yolov8-cls-resnet50.yaml
@@ -0,0 +1,28 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8-cls image classification model with ResNet50 backbone
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/classify
+
+# Parameters
+nc: 1000 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024]
+ s: [0.33, 0.50, 1024]
+ m: [0.67, 0.75, 1024]
+ l: [1.00, 1.00, 1024]
+ x: [1.00, 1.25, 1024]
+
+# YOLOv8.0n backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, ResNetLayer, [3, 64, 1, True, 1]] # 0-P1/2
+ - [-1, 1, ResNetLayer, [64, 64, 1, False, 3]] # 1-P2/4
+ - [-1, 1, ResNetLayer, [256, 128, 2, False, 4]] # 2-P3/8
+ - [-1, 1, ResNetLayer, [512, 256, 2, False, 6]] # 3-P4/16
+ - [-1, 1, ResNetLayer, [1024, 512, 2, False, 3]] # 4-P5/32
+
+# YOLOv8.0n head
+head:
+ - [-1, 1, Classify, [nc]] # Classify
diff --git a/ultralytics/cfg/models/v8/yolov8-cls.yaml b/ultralytics/cfg/models/v8/yolov8-cls.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e346e5e1b76164894a01f0640d30145d4161ce53
--- /dev/null
+++ b/ultralytics/cfg/models/v8/yolov8-cls.yaml
@@ -0,0 +1,32 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8-cls image classification model with YOLO backbone
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/classify
+
+# Parameters
+nc: 1000 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n-cls.yaml' will call yolov8-cls.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024]
+ s: [0.33, 0.50, 1024]
+ m: [0.67, 0.75, 1024]
+ l: [1.00, 1.00, 1024]
+ x: [1.00, 1.25, 1024]
+
+# YOLOv8.0n backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C2f, [128, True]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C2f, [256, True]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C2f, [512, True]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+ - [-1, 3, C2f, [1024, True]]
+
+# YOLOv8.0n head
+head:
+ - [-1, 1, Classify, [nc]] # Classify
diff --git a/ultralytics/cfg/models/v8/yolov8-ghost-p2.yaml b/ultralytics/cfg/models/v8/yolov8-ghost-p2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a98f23837bf0b82a6522303e731c4c32fc1f963f
--- /dev/null
+++ b/ultralytics/cfg/models/v8/yolov8-ghost-p2.yaml
@@ -0,0 +1,58 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8 object detection model with P2/4 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/detect
+# Employs Ghost convolutions and modules proposed in Huawei's GhostNet in https://arxiv.org/abs/1911.11907v2
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024] # YOLOv8n-ghost-p2 summary: 491 layers, 2033944 parameters, 2033928 gradients, 13.8 GFLOPs
+ s: [0.33, 0.50, 1024] # YOLOv8s-ghost-p2 summary: 491 layers, 5562080 parameters, 5562064 gradients, 25.1 GFLOPs
+ m: [0.67, 0.75, 768] # YOLOv8m-ghost-p2 summary: 731 layers, 9031728 parameters, 9031712 gradients, 42.8 GFLOPs
+ l: [1.00, 1.00, 512] # YOLOv8l-ghost-p2 summary: 971 layers, 12214448 parameters, 12214432 gradients, 69.1 GFLOPs
+ x: [1.00, 1.25, 512] # YOLOv8x-ghost-p2 summary: 971 layers, 18664776 parameters, 18664760 gradients, 103.3 GFLOPs
+
+# YOLOv8.0-ghost backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C3Ghost, [128, True]]
+ - [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C3Ghost, [256, True]]
+ - [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C3Ghost, [512, True]]
+ - [-1, 1, GhostConv, [1024, 3, 2]] # 7-P5/32
+ - [-1, 3, C3Ghost, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+
+# YOLOv8.0-ghost-p2 head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C3Ghost, [512]] # 12
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C3Ghost, [256]] # 15 (P3/8-small)
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 2], 1, Concat, [1]] # cat backbone P2
+ - [-1, 3, C3Ghost, [128]] # 18 (P2/4-xsmall)
+
+ - [-1, 1, GhostConv, [128, 3, 2]]
+ - [[-1, 15], 1, Concat, [1]] # cat head P3
+ - [-1, 3, C3Ghost, [256]] # 21 (P3/8-small)
+
+ - [-1, 1, GhostConv, [256, 3, 2]]
+ - [[-1, 12], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C3Ghost, [512]] # 24 (P4/16-medium)
+
+ - [-1, 1, GhostConv, [512, 3, 2]]
+ - [[-1, 9], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C3Ghost, [1024]] # 27 (P5/32-large)
+
+ - [[18, 21, 24, 27], 1, Detect, [nc]] # Detect(P2, P3, P4, P5)
diff --git a/ultralytics/cfg/models/v8/yolov8-ghost-p6.yaml b/ultralytics/cfg/models/v8/yolov8-ghost-p6.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..956c2f0ad668aa81ba6f2116f24325457caf330b
--- /dev/null
+++ b/ultralytics/cfg/models/v8/yolov8-ghost-p6.yaml
@@ -0,0 +1,60 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8 object detection model with P3/8 - P6/64 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/detect
+# Employs Ghost convolutions and modules proposed in Huawei's GhostNet in https://arxiv.org/abs/1911.11907v2
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024] # YOLOv8n-ghost-p6 summary: 529 layers, 2901100 parameters, 2901084 gradients, 5.8 GFLOPs
+ s: [0.33, 0.50, 1024] # YOLOv8s-ghost-p6 summary: 529 layers, 9520008 parameters, 9519992 gradients, 16.4 GFLOPs
+ m: [0.67, 0.75, 768] # YOLOv8m-ghost-p6 summary: 789 layers, 18002904 parameters, 18002888 gradients, 34.4 GFLOPs
+ l: [1.00, 1.00, 512] # YOLOv8l-ghost-p6 summary: 1049 layers, 21227584 parameters, 21227568 gradients, 55.3 GFLOPs
+ x: [1.00, 1.25, 512] # YOLOv8x-ghost-p6 summary: 1049 layers, 33057852 parameters, 33057836 gradients, 85.7 GFLOPs
+
+# YOLOv8.0-ghost backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C3Ghost, [128, True]]
+ - [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C3Ghost, [256, True]]
+ - [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C3Ghost, [512, True]]
+ - [-1, 1, GhostConv, [768, 3, 2]] # 7-P5/32
+ - [-1, 3, C3Ghost, [768, True]]
+ - [-1, 1, GhostConv, [1024, 3, 2]] # 9-P6/64
+ - [-1, 3, C3Ghost, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 11
+
+# YOLOv8.0-ghost-p6 head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 8], 1, Concat, [1]] # cat backbone P5
+ - [-1, 3, C3Ghost, [768]] # 14
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C3Ghost, [512]] # 17
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C3Ghost, [256]] # 20 (P3/8-small)
+
+ - [-1, 1, GhostConv, [256, 3, 2]]
+ - [[-1, 17], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C3Ghost, [512]] # 23 (P4/16-medium)
+
+ - [-1, 1, GhostConv, [512, 3, 2]]
+ - [[-1, 14], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C3Ghost, [768]] # 26 (P5/32-large)
+
+ - [-1, 1, GhostConv, [768, 3, 2]]
+ - [[-1, 11], 1, Concat, [1]] # cat head P6
+ - [-1, 3, C3Ghost, [1024]] # 29 (P6/64-xlarge)
+
+ - [[20, 23, 26, 29], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)
diff --git a/ultralytics/cfg/models/v8/yolov8-ghost.yaml b/ultralytics/cfg/models/v8/yolov8-ghost.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5888fb39bd0528995b3be15a82b7626661cb7358
--- /dev/null
+++ b/ultralytics/cfg/models/v8/yolov8-ghost.yaml
@@ -0,0 +1,50 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8 object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/detect
+# Employs Ghost convolutions and modules proposed in Huawei's GhostNet in https://arxiv.org/abs/1911.11907v2
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024] # YOLOv8n-ghost summary: 403 layers, 1865316 parameters, 1865300 gradients, 5.8 GFLOPs
+ s: [0.33, 0.50, 1024] # YOLOv8s-ghost summary: 403 layers, 5960072 parameters, 5960056 gradients, 16.4 GFLOPs
+ m: [0.67, 0.75, 768] # YOLOv8m-ghost summary: 603 layers, 10336312 parameters, 10336296 gradients, 32.7 GFLOPs
+ l: [1.00, 1.00, 512] # YOLOv8l-ghost summary: 803 layers, 14277872 parameters, 14277856 gradients, 53.7 GFLOPs
+ x: [1.00, 1.25, 512] # YOLOv8x-ghost summary: 803 layers, 22229308 parameters, 22229292 gradients, 83.3 GFLOPs
+
+# YOLOv8.0n-ghost backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, GhostConv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C3Ghost, [128, True]]
+ - [-1, 1, GhostConv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C3Ghost, [256, True]]
+ - [-1, 1, GhostConv, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C3Ghost, [512, True]]
+ - [-1, 1, GhostConv, [1024, 3, 2]] # 7-P5/32
+ - [-1, 3, C3Ghost, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+
+# YOLOv8.0n head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C3Ghost, [512]] # 12
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C3Ghost, [256]] # 15 (P3/8-small)
+
+ - [-1, 1, GhostConv, [256, 3, 2]]
+ - [[-1, 12], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C3Ghost, [512]] # 18 (P4/16-medium)
+
+ - [-1, 1, GhostConv, [512, 3, 2]]
+ - [[-1, 9], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C3Ghost, [1024]] # 21 (P5/32-large)
+
+ - [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v8/yolov8-obb.yaml b/ultralytics/cfg/models/v8/yolov8-obb.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..909324c5bec4190e86626599adb71c78b8957f04
--- /dev/null
+++ b/ultralytics/cfg/models/v8/yolov8-obb.yaml
@@ -0,0 +1,49 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8-obb Oriented Bounding Boxes (OBB) model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/obb
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
+ s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
+ m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
+ l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
+ x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
+
+# YOLOv8.0n backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C2f, [128, True]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C2f, [256, True]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C2f, [512, True]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+ - [-1, 3, C2f, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+
+# YOLOv8.0n head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C2f, [512]] # 12
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C2f, [256]] # 15 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 12], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C2f, [512]] # 18 (P4/16-medium)
+
+ - [-1, 1, Conv, [512, 3, 2]]
+ - [[-1, 9], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C2f, [1024]] # 21 (P5/32-large)
+
+ - [[15, 18, 21], 1, OBB, [nc, 1]] # OBB(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v8/yolov8-p2.yaml b/ultralytics/cfg/models/v8/yolov8-p2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..676bc8348c4b264e03140f9d4ec2da7a65b02a8b
--- /dev/null
+++ b/ultralytics/cfg/models/v8/yolov8-p2.yaml
@@ -0,0 +1,57 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8 object detection model with P2/4 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024]
+ s: [0.33, 0.50, 1024]
+ m: [0.67, 0.75, 768]
+ l: [1.00, 1.00, 512]
+ x: [1.00, 1.25, 512]
+
+# YOLOv8.0 backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C2f, [128, True]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C2f, [256, True]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C2f, [512, True]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+ - [-1, 3, C2f, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+
+# YOLOv8.0-p2 head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C2f, [512]] # 12
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C2f, [256]] # 15 (P3/8-small)
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 2], 1, Concat, [1]] # cat backbone P2
+ - [-1, 3, C2f, [128]] # 18 (P2/4-xsmall)
+
+ - [-1, 1, Conv, [128, 3, 2]]
+ - [[-1, 15], 1, Concat, [1]] # cat head P3
+ - [-1, 3, C2f, [256]] # 21 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 12], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C2f, [512]] # 24 (P4/16-medium)
+
+ - [-1, 1, Conv, [512, 3, 2]]
+ - [[-1, 9], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C2f, [1024]] # 27 (P5/32-large)
+
+ - [[18, 21, 24, 27], 1, Detect, [nc]] # Detect(P2, P3, P4, P5)
diff --git a/ultralytics/cfg/models/v8/yolov8-p6.yaml b/ultralytics/cfg/models/v8/yolov8-p6.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3fde34981f80238aa213c0e331f3488335af0a9c
--- /dev/null
+++ b/ultralytics/cfg/models/v8/yolov8-p6.yaml
@@ -0,0 +1,59 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8 object detection model with P3/8 - P6/64 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024] # YOLOv8n-p6 summary (fused): 220 layers, 4976656 parameters, 42560 gradients, 8.7 GFLOPs
+ s: [0.33, 0.50, 1024] # YOLOv8s-p6 summary (fused): 220 layers, 17897168 parameters, 57920 gradients, 28.5 GFLOPs
+ m: [0.67, 0.75, 768] # YOLOv8m-p6 summary (fused): 285 layers, 44862352 parameters, 78400 gradients, 83.1 GFLOPs
+ l: [1.00, 1.00, 512] # YOLOv8l-p6 summary (fused): 350 layers, 62351440 parameters, 98880 gradients, 167.3 GFLOPs
+ x: [1.00, 1.25, 512] # YOLOv8x-p6 summary (fused): 350 layers, 97382352 parameters, 123456 gradients, 261.1 GFLOPs
+
+# YOLOv8.0x6 backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C2f, [128, True]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C2f, [256, True]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C2f, [512, True]]
+ - [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
+ - [-1, 3, C2f, [768, True]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
+ - [-1, 3, C2f, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 11
+
+# YOLOv8.0x6 head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 8], 1, Concat, [1]] # cat backbone P5
+ - [-1, 3, C2, [768, False]] # 14
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C2, [512, False]] # 17
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C2, [256, False]] # 20 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 17], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
+
+ - [-1, 1, Conv, [512, 3, 2]]
+ - [[-1, 14], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C2, [768, False]] # 26 (P5/32-large)
+
+ - [-1, 1, Conv, [768, 3, 2]]
+ - [[-1, 11], 1, Concat, [1]] # cat head P6
+ - [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
+
+ - [[20, 23, 26, 29], 1, Detect, [nc]] # Detect(P3, P4, P5, P6)
diff --git a/ultralytics/cfg/models/v8/yolov8-pose-p6.yaml b/ultralytics/cfg/models/v8/yolov8-pose-p6.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..447a21aab0703b0f3a6520ba85b112ff6bd4f809
--- /dev/null
+++ b/ultralytics/cfg/models/v8/yolov8-pose-p6.yaml
@@ -0,0 +1,60 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8-pose keypoints/pose estimation model with P3/8 - P6/64 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/pose
+
+# Parameters
+nc: 1 # number of classes
+kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+scales: # model compound scaling constants, i.e. 'model=yolov8n-p6.yaml' will call yolov8-p6.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024]
+ s: [0.33, 0.50, 1024]
+ m: [0.67, 0.75, 768]
+ l: [1.00, 1.00, 512]
+ x: [1.00, 1.25, 512]
+
+# YOLOv8.0x6 backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C2f, [128, True]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C2f, [256, True]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C2f, [512, True]]
+ - [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
+ - [-1, 3, C2f, [768, True]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
+ - [-1, 3, C2f, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 11
+
+# YOLOv8.0x6 head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 8], 1, Concat, [1]] # cat backbone P5
+ - [-1, 3, C2, [768, False]] # 14
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C2, [512, False]] # 17
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C2, [256, False]] # 20 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 17], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
+
+ - [-1, 1, Conv, [512, 3, 2]]
+ - [[-1, 14], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C2, [768, False]] # 26 (P5/32-large)
+
+ - [-1, 1, Conv, [768, 3, 2]]
+ - [[-1, 11], 1, Concat, [1]] # cat head P6
+ - [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
+
+ - [[20, 23, 26, 29], 1, Pose, [nc, kpt_shape]] # Pose(P3, P4, P5, P6)
diff --git a/ultralytics/cfg/models/v8/yolov8-pose.yaml b/ultralytics/cfg/models/v8/yolov8-pose.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c22bc435b578d58b1a432e1ee0f203562e73f076
--- /dev/null
+++ b/ultralytics/cfg/models/v8/yolov8-pose.yaml
@@ -0,0 +1,50 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8-pose keypoints/pose estimation model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/pose
+
+# Parameters
+nc: 1 # number of classes
+kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+scales: # model compound scaling constants, i.e. 'model=yolov8n-pose.yaml' will call yolov8-pose.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024]
+ s: [0.33, 0.50, 1024]
+ m: [0.67, 0.75, 768]
+ l: [1.00, 1.00, 512]
+ x: [1.00, 1.25, 512]
+
+# YOLOv8.0n backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C2f, [128, True]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C2f, [256, True]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C2f, [512, True]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+ - [-1, 3, C2f, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+
+# YOLOv8.0n head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C2f, [512]] # 12
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C2f, [256]] # 15 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 12], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C2f, [512]] # 18 (P4/16-medium)
+
+ - [-1, 1, Conv, [512, 3, 2]]
+ - [[-1, 9], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C2f, [1024]] # 21 (P5/32-large)
+
+ - [[15, 18, 21], 1, Pose, [nc, kpt_shape]] # Pose(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v8/yolov8-rtdetr.yaml b/ultralytics/cfg/models/v8/yolov8-rtdetr.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..50ec129ac1885898d1ca9b3bc94a196e92519efb
--- /dev/null
+++ b/ultralytics/cfg/models/v8/yolov8-rtdetr.yaml
@@ -0,0 +1,49 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8-RTDETR hybrid object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/rtdetr
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
+ s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
+ m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
+ l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
+ x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
+
+# YOLOv8.0n backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C2f, [128, True]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C2f, [256, True]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C2f, [512, True]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+ - [-1, 3, C2f, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+
+# YOLOv8.0n head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C2f, [512]] # 12
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C2f, [256]] # 15 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 12], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C2f, [512]] # 18 (P4/16-medium)
+
+ - [-1, 1, Conv, [512, 3, 2]]
+ - [[-1, 9], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C2f, [1024]] # 21 (P5/32-large)
+
+ - [[15, 18, 21], 1, RTDETRDecoder, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v8/yolov8-seg-p6.yaml b/ultralytics/cfg/models/v8/yolov8-seg-p6.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4c7ba9bf4dddaa23adc5521ff51ab149ae09495a
--- /dev/null
+++ b/ultralytics/cfg/models/v8/yolov8-seg-p6.yaml
@@ -0,0 +1,59 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8-seg instance segmentation model with P3/8 - P6/64 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/segment
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n-seg-p6.yaml' will call yolov8-seg-p6.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024]
+ s: [0.33, 0.50, 1024]
+ m: [0.67, 0.75, 768]
+ l: [1.00, 1.00, 512]
+ x: [1.00, 1.25, 512]
+
+# YOLOv8.0x6 backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C2f, [128, True]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C2f, [256, True]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C2f, [512, True]]
+ - [-1, 1, Conv, [768, 3, 2]] # 7-P5/32
+ - [-1, 3, C2f, [768, True]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 9-P6/64
+ - [-1, 3, C2f, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 11
+
+# YOLOv8.0x6 head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 8], 1, Concat, [1]] # cat backbone P5
+ - [-1, 3, C2, [768, False]] # 14
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C2, [512, False]] # 17
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C2, [256, False]] # 20 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 17], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C2, [512, False]] # 23 (P4/16-medium)
+
+ - [-1, 1, Conv, [512, 3, 2]]
+ - [[-1, 14], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C2, [768, False]] # 26 (P5/32-large)
+
+ - [-1, 1, Conv, [768, 3, 2]]
+ - [[-1, 11], 1, Concat, [1]] # cat head P6
+ - [-1, 3, C2, [1024, False]] # 29 (P6/64-xlarge)
+
+ - [[20, 23, 26, 29], 1, Segment, [nc, 32, 256]] # Pose(P3, P4, P5, P6)
diff --git a/ultralytics/cfg/models/v8/yolov8-seg.yaml b/ultralytics/cfg/models/v8/yolov8-seg.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..52b1c7e9aedc4fbc04d5c4f48f4ad214ad6a51e2
--- /dev/null
+++ b/ultralytics/cfg/models/v8/yolov8-seg.yaml
@@ -0,0 +1,49 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8-seg instance segmentation model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/segment
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n-seg.yaml' will call yolov8-seg.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024]
+ s: [0.33, 0.50, 1024]
+ m: [0.67, 0.75, 768]
+ l: [1.00, 1.00, 512]
+ x: [1.00, 1.25, 512]
+
+# YOLOv8.0n backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C2f, [128, True]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C2f, [256, True]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C2f, [512, True]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+ - [-1, 3, C2f, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+
+# YOLOv8.0n head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C2f, [512]] # 12
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C2f, [256]] # 15 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 12], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C2f, [512]] # 18 (P4/16-medium)
+
+ - [-1, 1, Conv, [512, 3, 2]]
+ - [[-1, 9], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C2f, [1024]] # 21 (P5/32-large)
+
+ - [[15, 18, 21], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v8/yolov8-world.yaml b/ultralytics/cfg/models/v8/yolov8-world.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3c92e824ce684207ae08d9f5f48310c51208aed5
--- /dev/null
+++ b/ultralytics/cfg/models/v8/yolov8-world.yaml
@@ -0,0 +1,51 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8-World hybrid object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo-world
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
+ s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
+ m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
+ l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
+ x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
+
+# YOLOv8.0n backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C2f, [128, True]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C2f, [256, True]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C2f, [512, True]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+ - [-1, 3, C2f, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+
+# YOLOv8.0n head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C2fAttn, [512, 256, 8]] # 12
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C2fAttn, [256, 128, 4]] # 15 (P3/8-small)
+
+ - [[15, 12, 9], 1, ImagePoolingAttn, [256]] # 16 (P3/8-small)
+
+ - [15, 1, Conv, [256, 3, 2]]
+ - [[-1, 12], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C2fAttn, [512, 256, 8]] # 19 (P4/16-medium)
+
+ - [-1, 1, Conv, [512, 3, 2]]
+ - [[-1, 9], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C2fAttn, [1024, 512, 16]] # 22 (P5/32-large)
+
+ - [[15, 19, 22], 1, WorldDetect, [nc, 512, False]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v8/yolov8-worldv2.yaml b/ultralytics/cfg/models/v8/yolov8-worldv2.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c6aaa2773315acca026287894a07fd5ad0f2559e
--- /dev/null
+++ b/ultralytics/cfg/models/v8/yolov8-worldv2.yaml
@@ -0,0 +1,49 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8-Worldv2 hybrid object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolo-world
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
+ s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
+ m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
+ l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
+ x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
+
+# YOLOv8.0n backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C2f, [128, True]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C2f, [256, True]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C2f, [512, True]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+ - [-1, 3, C2f, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+
+# YOLOv8.0n head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C2fAttn, [512, 256, 8]] # 12
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C2fAttn, [256, 128, 4]] # 15 (P3/8-small)
+
+ - [15, 1, Conv, [256, 3, 2]]
+ - [[-1, 12], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C2fAttn, [512, 256, 8]] # 18 (P4/16-medium)
+
+ - [-1, 1, Conv, [512, 3, 2]]
+ - [[-1, 9], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C2fAttn, [1024, 512, 16]] # 21 (P5/32-large)
+
+ - [[15, 18, 21], 1, WorldDetect, [nc, 512, True]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v8/yolov8.yaml b/ultralytics/cfg/models/v8/yolov8.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c7b9938ec34d4e2529821a76d6de52f2accbac06
--- /dev/null
+++ b/ultralytics/cfg/models/v8/yolov8.yaml
@@ -0,0 +1,49 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Ultralytics YOLOv8 object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov8
+# Task docs: https://docs.ultralytics.com/tasks/detect
+
+# Parameters
+nc: 80 # number of classes
+scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
+ # [depth, width, max_channels]
+ n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
+ s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
+ m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
+ l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
+ x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
+
+# YOLOv8.0n backbone
+backbone:
+ # [from, repeats, module, args]
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 3, C2f, [128, True]]
+ - [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
+ - [-1, 6, C2f, [256, True]]
+ - [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
+ - [-1, 6, C2f, [512, True]]
+ - [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
+ - [-1, 3, C2f, [1024, True]]
+ - [-1, 1, SPPF, [1024, 5]] # 9
+
+# YOLOv8.0n head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 3, C2f, [512]] # 12
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 3, C2f, [256]] # 15 (P3/8-small)
+
+ - [-1, 1, Conv, [256, 3, 2]]
+ - [[-1, 12], 1, Concat, [1]] # cat head P4
+ - [-1, 3, C2f, [512]] # 18 (P4/16-medium)
+
+ - [-1, 1, Conv, [512, 3, 2]]
+ - [[-1, 9], 1, Concat, [1]] # cat head P5
+ - [-1, 3, C2f, [1024]] # 21 (P5/32-large)
+
+ - [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v9/yolov9c-seg.yaml b/ultralytics/cfg/models/v9/yolov9c-seg.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..14122cb83933b17f6b56a703cf8dd64bbd3c3fbc
--- /dev/null
+++ b/ultralytics/cfg/models/v9/yolov9c-seg.yaml
@@ -0,0 +1,41 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLOv9c-seg instance segmentation model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov9
+# Task docs: https://docs.ultralytics.com/tasks/segment
+# 654 layers, 27897120 parameters, 159.4 GFLOPs
+
+# Parameters
+nc: 80 # number of classes
+
+# GELAN backbone
+backbone:
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]] # 2
+ - [-1, 1, ADown, [256]] # 3-P3/8
+ - [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]] # 4
+ - [-1, 1, ADown, [512]] # 5-P4/16
+ - [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 6
+ - [-1, 1, ADown, [512]] # 7-P5/32
+ - [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 8
+ - [-1, 1, SPPELAN, [512, 256]] # 9
+
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 12
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]] # 15 (P3/8-small)
+
+ - [-1, 1, ADown, [256]]
+ - [[-1, 12], 1, Concat, [1]] # cat head P4
+ - [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 18 (P4/16-medium)
+
+ - [-1, 1, ADown, [512]]
+ - [[-1, 9], 1, Concat, [1]] # cat head P5
+ - [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 21 (P5/32-large)
+
+ - [[15, 18, 21], 1, Segment, [nc, 32, 256]] # Segment(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v9/yolov9c.yaml b/ultralytics/cfg/models/v9/yolov9c.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4fc1fcd13fdf945d5a91269065ae3b33d2f2a76f
--- /dev/null
+++ b/ultralytics/cfg/models/v9/yolov9c.yaml
@@ -0,0 +1,41 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLOv9c object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov9
+# Task docs: https://docs.ultralytics.com/tasks/detect
+# 618 layers, 25590912 parameters, 104.0 GFLOPs
+
+# Parameters
+nc: 80 # number of classes
+
+# GELAN backbone
+backbone:
+ - [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
+ - [-1, 1, RepNCSPELAN4, [256, 128, 64, 1]] # 2
+ - [-1, 1, ADown, [256]] # 3-P3/8
+ - [-1, 1, RepNCSPELAN4, [512, 256, 128, 1]] # 4
+ - [-1, 1, ADown, [512]] # 5-P4/16
+ - [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 6
+ - [-1, 1, ADown, [512]] # 7-P5/32
+ - [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 8
+ - [-1, 1, SPPELAN, [512, 256]] # 9
+
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 12
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 1, RepNCSPELAN4, [256, 256, 128, 1]] # 15 (P3/8-small)
+
+ - [-1, 1, ADown, [256]]
+ - [[-1, 12], 1, Concat, [1]] # cat head P4
+ - [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 18 (P4/16-medium)
+
+ - [-1, 1, ADown, [512]]
+ - [[-1, 9], 1, Concat, [1]] # cat head P5
+ - [-1, 1, RepNCSPELAN4, [512, 512, 256, 1]] # 21 (P5/32-large)
+
+ - [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v9/yolov9e-seg.yaml b/ultralytics/cfg/models/v9/yolov9e-seg.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4361daac29358984827fea4c1eaa3478324a033a
--- /dev/null
+++ b/ultralytics/cfg/models/v9/yolov9e-seg.yaml
@@ -0,0 +1,64 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLOv9e-seg instance segmentation model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov9
+# Task docs: https://docs.ultralytics.com/tasks/segment
+# 1261 layers, 60512800 parameters, 248.4 GFLOPs
+
+# Parameters
+nc: 80 # number of classes
+
+# GELAN backbone
+backbone:
+ - [-1, 1, nn.Identity, []]
+ - [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 2-P2/4
+ - [-1, 1, RepNCSPELAN4, [256, 128, 64, 2]] # 3
+ - [-1, 1, ADown, [256]] # 4-P3/8
+ - [-1, 1, RepNCSPELAN4, [512, 256, 128, 2]] # 5
+ - [-1, 1, ADown, [512]] # 6-P4/16
+ - [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 7
+ - [-1, 1, ADown, [1024]] # 8-P5/32
+ - [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 9
+
+ - [1, 1, CBLinear, [[64]]] # 10
+ - [3, 1, CBLinear, [[64, 128]]] # 11
+ - [5, 1, CBLinear, [[64, 128, 256]]] # 12
+ - [7, 1, CBLinear, [[64, 128, 256, 512]]] # 13
+ - [9, 1, CBLinear, [[64, 128, 256, 512, 1024]]] # 14
+
+ - [0, 1, Conv, [64, 3, 2]] # 15-P1/2
+ - [[10, 11, 12, 13, 14, -1], 1, CBFuse, [[0, 0, 0, 0, 0]]] # 16
+ - [-1, 1, Conv, [128, 3, 2]] # 17-P2/4
+ - [[11, 12, 13, 14, -1], 1, CBFuse, [[1, 1, 1, 1]]] # 18
+ - [-1, 1, RepNCSPELAN4, [256, 128, 64, 2]] # 19
+ - [-1, 1, ADown, [256]] # 20-P3/8
+ - [[12, 13, 14, -1], 1, CBFuse, [[2, 2, 2]]] # 21
+ - [-1, 1, RepNCSPELAN4, [512, 256, 128, 2]] # 22
+ - [-1, 1, ADown, [512]] # 23-P4/16
+ - [[13, 14, -1], 1, CBFuse, [[3, 3]]] # 24
+ - [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 25
+ - [-1, 1, ADown, [1024]] # 26-P5/32
+ - [[14, -1], 1, CBFuse, [[4]]] # 27
+ - [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 28
+ - [-1, 1, SPPELAN, [512, 256]] # 29
+
+# GELAN head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 25], 1, Concat, [1]] # cat backbone P4
+ - [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]] # 32
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 22], 1, Concat, [1]] # cat backbone P3
+ - [-1, 1, RepNCSPELAN4, [256, 256, 128, 2]] # 35 (P3/8-small)
+
+ - [-1, 1, ADown, [256]]
+ - [[-1, 32], 1, Concat, [1]] # cat head P4
+ - [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]] # 38 (P4/16-medium)
+
+ - [-1, 1, ADown, [512]]
+ - [[-1, 29], 1, Concat, [1]] # cat head P5
+ - [-1, 1, RepNCSPELAN4, [512, 1024, 512, 2]] # 41 (P5/32-large)
+
+ - [[35, 38, 41], 1, Segment, [nc, 32, 256]] # Segment (P3, P4, P5)
diff --git a/ultralytics/cfg/models/v9/yolov9e.yaml b/ultralytics/cfg/models/v9/yolov9e.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..bba5597d0cf9d848925b30afe1e07c760cf8a62e
--- /dev/null
+++ b/ultralytics/cfg/models/v9/yolov9e.yaml
@@ -0,0 +1,64 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLOv9e object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov9
+# Task docs: https://docs.ultralytics.com/tasks/detect
+# 1225 layers, 58206592 parameters, 193.0 GFLOPs
+
+# Parameters
+nc: 80 # number of classes
+
+# GELAN backbone
+backbone:
+ - [-1, 1, nn.Identity, []]
+ - [-1, 1, Conv, [64, 3, 2]] # 1-P1/2
+ - [-1, 1, Conv, [128, 3, 2]] # 2-P2/4
+ - [-1, 1, RepNCSPELAN4, [256, 128, 64, 2]] # 3
+ - [-1, 1, ADown, [256]] # 4-P3/8
+ - [-1, 1, RepNCSPELAN4, [512, 256, 128, 2]] # 5
+ - [-1, 1, ADown, [512]] # 6-P4/16
+ - [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 7
+ - [-1, 1, ADown, [1024]] # 8-P5/32
+ - [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 9
+
+ - [1, 1, CBLinear, [[64]]] # 10
+ - [3, 1, CBLinear, [[64, 128]]] # 11
+ - [5, 1, CBLinear, [[64, 128, 256]]] # 12
+ - [7, 1, CBLinear, [[64, 128, 256, 512]]] # 13
+ - [9, 1, CBLinear, [[64, 128, 256, 512, 1024]]] # 14
+
+ - [0, 1, Conv, [64, 3, 2]] # 15-P1/2
+ - [[10, 11, 12, 13, 14, -1], 1, CBFuse, [[0, 0, 0, 0, 0]]] # 16
+ - [-1, 1, Conv, [128, 3, 2]] # 17-P2/4
+ - [[11, 12, 13, 14, -1], 1, CBFuse, [[1, 1, 1, 1]]] # 18
+ - [-1, 1, RepNCSPELAN4, [256, 128, 64, 2]] # 19
+ - [-1, 1, ADown, [256]] # 20-P3/8
+ - [[12, 13, 14, -1], 1, CBFuse, [[2, 2, 2]]] # 21
+ - [-1, 1, RepNCSPELAN4, [512, 256, 128, 2]] # 22
+ - [-1, 1, ADown, [512]] # 23-P4/16
+ - [[13, 14, -1], 1, CBFuse, [[3, 3]]] # 24
+ - [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 25
+ - [-1, 1, ADown, [1024]] # 26-P5/32
+ - [[14, -1], 1, CBFuse, [[4]]] # 27
+ - [-1, 1, RepNCSPELAN4, [1024, 512, 256, 2]] # 28
+ - [-1, 1, SPPELAN, [512, 256]] # 29
+
+# GELAN head
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 25], 1, Concat, [1]] # cat backbone P4
+ - [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]] # 32
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 22], 1, Concat, [1]] # cat backbone P3
+ - [-1, 1, RepNCSPELAN4, [256, 256, 128, 2]] # 35 (P3/8-small)
+
+ - [-1, 1, ADown, [256]]
+ - [[-1, 32], 1, Concat, [1]] # cat head P4
+ - [-1, 1, RepNCSPELAN4, [512, 512, 256, 2]] # 38 (P4/16-medium)
+
+ - [-1, 1, ADown, [512]]
+ - [[-1, 29], 1, Concat, [1]] # cat head P5
+ - [-1, 1, RepNCSPELAN4, [512, 1024, 512, 2]] # 41 (P5/32-large)
+
+ - [[35, 38, 41], 1, Detect, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v9/yolov9m.yaml b/ultralytics/cfg/models/v9/yolov9m.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..89bed65bebbed3d42eb804d19bcf8eefca7791f2
--- /dev/null
+++ b/ultralytics/cfg/models/v9/yolov9m.yaml
@@ -0,0 +1,41 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLOv9m object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov9
+# Task docs: https://docs.ultralytics.com/tasks/detect
+# 603 layers, 20216160 parameters, 77.9 GFLOPs
+
+# Parameters
+nc: 80 # number of classes
+
+# GELAN backbone
+backbone:
+ - [-1, 1, Conv, [32, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [64, 3, 2]] # 1-P2/4
+ - [-1, 1, RepNCSPELAN4, [128, 128, 64, 1]] # 2
+ - [-1, 1, AConv, [240]] # 3-P3/8
+ - [-1, 1, RepNCSPELAN4, [240, 240, 120, 1]] # 4
+ - [-1, 1, AConv, [360]] # 5-P4/16
+ - [-1, 1, RepNCSPELAN4, [360, 360, 180, 1]] # 6
+ - [-1, 1, AConv, [480]] # 7-P5/32
+ - [-1, 1, RepNCSPELAN4, [480, 480, 240, 1]] # 8
+ - [-1, 1, SPPELAN, [480, 240]] # 9
+
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 1, RepNCSPELAN4, [360, 360, 180, 1]] # 12
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 1, RepNCSPELAN4, [240, 240, 120, 1]] # 15
+
+ - [-1, 1, AConv, [180]]
+ - [[-1, 12], 1, Concat, [1]] # cat head P4
+ - [-1, 1, RepNCSPELAN4, [360, 360, 180, 1]] # 18 (P4/16-medium)
+
+ - [-1, 1, AConv, [240]]
+ - [[-1, 9], 1, Concat, [1]] # cat head P5
+ - [-1, 1, RepNCSPELAN4, [480, 480, 240, 1]] # 21 (P5/32-large)
+
+ - [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/models/v9/yolov9s.yaml b/ultralytics/cfg/models/v9/yolov9s.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..28891f4cebcdc3682bec403c539c79eb5bfe7834
--- /dev/null
+++ b/ultralytics/cfg/models/v9/yolov9s.yaml
@@ -0,0 +1,41 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLOv9s object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov9
+# Task docs: https://docs.ultralytics.com/tasks/detect
+# 917 layers, 7318368 parameters, 27.6 GFLOPs
+
+# Parameters
+nc: 80 # number of classes
+
+# GELAN backbone
+backbone:
+ - [-1, 1, Conv, [32, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [64, 3, 2]] # 1-P2/4
+ - [-1, 1, ELAN1, [64, 64, 32]] # 2
+ - [-1, 1, AConv, [128]] # 3-P3/8
+ - [-1, 1, RepNCSPELAN4, [128, 128, 64, 3]] # 4
+ - [-1, 1, AConv, [192]] # 5-P4/16
+ - [-1, 1, RepNCSPELAN4, [192, 192, 96, 3]] # 6
+ - [-1, 1, AConv, [256]] # 7-P5/32
+ - [-1, 1, RepNCSPELAN4, [256, 256, 128, 3]] # 8
+ - [-1, 1, SPPELAN, [256, 128]] # 9
+
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 1, RepNCSPELAN4, [192, 192, 96, 3]] # 12
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 1, RepNCSPELAN4, [128, 128, 64, 3]] # 15
+
+ - [-1, 1, AConv, [96]]
+ - [[-1, 12], 1, Concat, [1]] # cat head P4
+ - [-1, 1, RepNCSPELAN4, [192, 192, 96, 3]] # 18 (P4/16-medium)
+
+ - [-1, 1, AConv, [128]]
+ - [[-1, 9], 1, Concat, [1]] # cat head P5
+ - [-1, 1, RepNCSPELAN4, [256, 256, 128, 3]] # 21 (P5/32-large)
+
+ - [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4 P5)
diff --git a/ultralytics/cfg/models/v9/yolov9t.yaml b/ultralytics/cfg/models/v9/yolov9t.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..21a5bad86b93a15adc4fa00e749a4b5a0699abd3
--- /dev/null
+++ b/ultralytics/cfg/models/v9/yolov9t.yaml
@@ -0,0 +1,41 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# YOLOv9t object detection model with P3/8 - P5/32 outputs
+# Model docs: https://docs.ultralytics.com/models/yolov9
+# Task docs: https://docs.ultralytics.com/tasks/detect
+# 917 layers, 2128720 parameters, 8.5 GFLOPs
+
+# Parameters
+nc: 80 # number of classes
+
+# GELAN backbone
+backbone:
+ - [-1, 1, Conv, [16, 3, 2]] # 0-P1/2
+ - [-1, 1, Conv, [32, 3, 2]] # 1-P2/4
+ - [-1, 1, ELAN1, [32, 32, 16]] # 2
+ - [-1, 1, AConv, [64]] # 3-P3/8
+ - [-1, 1, RepNCSPELAN4, [64, 64, 32, 3]] # 4
+ - [-1, 1, AConv, [96]] # 5-P4/16
+ - [-1, 1, RepNCSPELAN4, [96, 96, 48, 3]] # 6
+ - [-1, 1, AConv, [128]] # 7-P5/32
+ - [-1, 1, RepNCSPELAN4, [128, 128, 64, 3]] # 8
+ - [-1, 1, SPPELAN, [128, 64]] # 9
+
+head:
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 6], 1, Concat, [1]] # cat backbone P4
+ - [-1, 1, RepNCSPELAN4, [96, 96, 48, 3]] # 12
+
+ - [-1, 1, nn.Upsample, [None, 2, "nearest"]]
+ - [[-1, 4], 1, Concat, [1]] # cat backbone P3
+ - [-1, 1, RepNCSPELAN4, [64, 64, 32, 3]] # 15
+
+ - [-1, 1, AConv, [48]]
+ - [[-1, 12], 1, Concat, [1]] # cat head P4
+ - [-1, 1, RepNCSPELAN4, [96, 96, 48, 3]] # 18 (P4/16-medium)
+
+ - [-1, 1, AConv, [64]]
+ - [[-1, 9], 1, Concat, [1]] # cat head P5
+ - [-1, 1, RepNCSPELAN4, [128, 128, 64, 3]] # 21 (P5/32-large)
+
+ - [[15, 18, 21], 1, Detect, [nc]] # Detect(P3, P4, P5)
diff --git a/ultralytics/cfg/solutions/default.yaml b/ultralytics/cfg/solutions/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a4afb49b324894c7caa6f6a1382cdc8acae10db9
--- /dev/null
+++ b/ultralytics/cfg/solutions/default.yaml
@@ -0,0 +1,24 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Global configuration YAML with settings and arguments for Ultralytics Solutions
+# For documentation see https://docs.ultralytics.com/solutions/
+
+# Object counting settings --------------------------------------------------------------------------------------------
+region: # list[tuple[int, int]] object counting, queue or speed estimation region points.
+show_in: True # (bool) flag to display objects moving *into* the defined region
+show_out: True # (bool) flag to display objects moving *out of* the defined region
+
+# Heatmaps settings ----------------------------------------------------------------------------------------------------
+colormap: # (int | str) colormap for heatmap, Only OPENCV supported colormaps can be used.
+
+# Workouts monitoring settings -----------------------------------------------------------------------------------------
+up_angle: 145.0 # (float) Workouts up_angle for counts, 145.0 is default value.
+down_angle: 90 # (float) Workouts down_angle for counts, 90 is default value. Y
+kpts: [6, 8, 10] # (list[int]) keypoints for workouts monitoring, i.e. for push-ups kpts have values of [6, 8, 10].
+
+# Analytics settings ---------------------------------------------------------------------------------------------------
+analytics_type: "line" # (str) analytics type i.e "line", "pie", "bar" or "area" charts.
+json_file: # (str) parking system regions file path.
+
+# Security alarm system settings ---------------------------------------------------------------------------------------
+records: 5 # (int) Total detections count to send an email about security
diff --git a/ultralytics/cfg/trackers/botsort.yaml b/ultralytics/cfg/trackers/botsort.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..aedcee4860fe00e969aca4c51ff0975d931ac283
--- /dev/null
+++ b/ultralytics/cfg/trackers/botsort.yaml
@@ -0,0 +1,21 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Default Ultralytics settings for BoT-SORT tracker when using mode="track"
+# For documentation and examples see https://docs.ultralytics.com/modes/track/
+# For BoT-SORT source code see https://github.com/NirAharon/BoT-SORT
+
+tracker_type: botsort # tracker type, ['botsort', 'bytetrack']
+track_high_thresh: 0.25 # threshold for the first association
+track_low_thresh: 0.1 # threshold for the second association
+new_track_thresh: 0.25 # threshold for init new track if the detection does not match any tracks
+track_buffer: 30 # buffer to calculate the time when to remove tracks
+match_thresh: 0.8 # threshold for matching tracks
+fuse_score: True # Whether to fuse confidence scores with the iou distances before matching
+# min_box_area: 10 # threshold for min box areas(for tracker evaluation, not used for now)
+
+# BoT-SORT settings
+gmc_method: sparseOptFlow # method of global motion compensation
+# ReID model related thresh (not supported yet)
+proximity_thresh: 0.5
+appearance_thresh: 0.25
+with_reid: False
diff --git a/ultralytics/cfg/trackers/bytetrack.yaml b/ultralytics/cfg/trackers/bytetrack.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..62071a3022da1a38fc8aaf74a538d3829a489502
--- /dev/null
+++ b/ultralytics/cfg/trackers/bytetrack.yaml
@@ -0,0 +1,14 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# Default Ultralytics settings for ByteTrack tracker when using mode="track"
+# For documentation and examples see https://docs.ultralytics.com/modes/track/
+# For ByteTrack source code see https://github.com/ifzhang/ByteTrack
+
+tracker_type: bytetrack # tracker type, ['botsort', 'bytetrack']
+track_high_thresh: 0.25 # threshold for the first association
+track_low_thresh: 0.1 # threshold for the second association
+new_track_thresh: 0.25 # threshold for init new track if the detection does not match any tracks
+track_buffer: 30 # buffer to calculate the time when to remove tracks
+match_thresh: 0.8 # threshold for matching tracks
+fuse_score: True # Whether to fuse confidence scores with the iou distances before matching
+# min_box_area: 10 # threshold for min box areas(for tracker evaluation, not used for now)
diff --git a/ultralytics/data/__init__.py b/ultralytics/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..a5d258d5df5f417903d2a42c9dce4174610a9804
--- /dev/null
+++ b/ultralytics/data/__init__.py
@@ -0,0 +1,26 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from .base import BaseDataset
+from .build import build_dataloader, build_grounding, build_yolo_dataset, load_inference_source
+from .dataset import (
+ ClassificationDataset,
+ GroundingDataset,
+ SemanticDataset,
+ YOLOConcatDataset,
+ YOLODataset,
+ YOLOMultiModalDataset,
+)
+
+__all__ = (
+ "BaseDataset",
+ "ClassificationDataset",
+ "SemanticDataset",
+ "YOLODataset",
+ "YOLOMultiModalDataset",
+ "YOLOConcatDataset",
+ "GroundingDataset",
+ "build_yolo_dataset",
+ "build_grounding",
+ "build_dataloader",
+ "load_inference_source",
+)
diff --git a/ultralytics/data/annotator.py b/ultralytics/data/annotator.py
new file mode 100644
index 0000000000000000000000000000000000000000..982e5de5ab1ed6e401bfea97d21548c47623ef09
--- /dev/null
+++ b/ultralytics/data/annotator.py
@@ -0,0 +1,72 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+from pathlib import Path
+
+from ultralytics import SAM, YOLO
+
+
+def auto_annotate(
+ data,
+ det_model="yolo11x.pt",
+ sam_model="sam_b.pt",
+ device="",
+ conf=0.25,
+ iou=0.45,
+ imgsz=640,
+ max_det=300,
+ classes=None,
+ output_dir=None,
+):
+ """
+ Automatically annotates images using a YOLO object detection model and a SAM segmentation model.
+
+ This function processes images in a specified directory, detects objects using a YOLO model, and then generates
+ segmentation masks using a SAM model. The resulting annotations are saved as text files.
+
+ Args:
+ data (str): Path to a folder containing images to be annotated.
+ det_model (str): Path or name of the pre-trained YOLO detection model.
+ sam_model (str): Path or name of the pre-trained SAM segmentation model.
+ device (str): Device to run the models on (e.g., 'cpu', 'cuda', '0').
+ conf (float): Confidence threshold for detection model; default is 0.25.
+ iou (float): IoU threshold for filtering overlapping boxes in detection results; default is 0.45.
+ imgsz (int): Input image resize dimension; default is 640.
+ max_det (int): Limits detections per image to control outputs in dense scenes.
+ classes (list): Filters predictions to specified class IDs, returning only relevant detections.
+ output_dir (str | None): Directory to save the annotated results. If None, a default directory is created.
+
+ Examples:
+ >>> from ultralytics.data.annotator import auto_annotate
+ >>> auto_annotate(data="ultralytics/assets", det_model="yolo11n.pt", sam_model="mobile_sam.pt")
+
+ Notes:
+ - The function creates a new directory for output if not specified.
+ - Annotation results are saved as text files with the same names as the input images.
+ - Each line in the output text file represents a detected object with its class ID and segmentation points.
+ """
+ det_model = YOLO(det_model)
+ sam_model = SAM(sam_model)
+
+ data = Path(data)
+ if not output_dir:
+ output_dir = data.parent / f"{data.stem}_auto_annotate_labels"
+ Path(output_dir).mkdir(exist_ok=True, parents=True)
+
+ det_results = det_model(
+ data, stream=True, device=device, conf=conf, iou=iou, imgsz=imgsz, max_det=max_det, classes=classes
+ )
+
+ for result in det_results:
+ class_ids = result.boxes.cls.int().tolist() # noqa
+ if len(class_ids):
+ boxes = result.boxes.xyxy # Boxes object for bbox outputs
+ sam_results = sam_model(result.orig_img, bboxes=boxes, verbose=False, save=False, device=device)
+ segments = sam_results[0].masks.xyn # noqa
+
+ with open(f"{Path(output_dir) / Path(result.path).stem}.txt", "w") as f:
+ for i in range(len(segments)):
+ s = segments[i]
+ if len(s) == 0:
+ continue
+ segment = map(str, segments[i].reshape(-1).tolist())
+ f.write(f"{class_ids[i]} " + " ".join(segment) + "\n")
diff --git a/ultralytics/data/augment.py b/ultralytics/data/augment.py
new file mode 100644
index 0000000000000000000000000000000000000000..85b90148c2a1934008da812023ef4b360a7572e6
--- /dev/null
+++ b/ultralytics/data/augment.py
@@ -0,0 +1,2744 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import math
+import random
+from copy import deepcopy
+from typing import Tuple, Union
+
+import cv2
+import numpy as np
+import torch
+from PIL import Image
+
+from ultralytics.data.utils import polygons2masks, polygons2masks_overlap
+from ultralytics.utils import LOGGER, colorstr
+from ultralytics.utils.checks import check_version
+from ultralytics.utils.instance import Instances
+from ultralytics.utils.metrics import bbox_ioa
+from ultralytics.utils.ops import segment2box, xyxyxyxy2xywhr
+from ultralytics.utils.torch_utils import TORCHVISION_0_10, TORCHVISION_0_11, TORCHVISION_0_13
+
+DEFAULT_MEAN = (0.0, 0.0, 0.0)
+DEFAULT_STD = (1.0, 1.0, 1.0)
+DEFAULT_CROP_FRACTION = 1.0
+
+
+class BaseTransform:
+ """
+ Base class for image transformations in the Ultralytics library.
+
+ This class serves as a foundation for implementing various image processing operations, designed to be
+ compatible with both classification and semantic segmentation tasks.
+
+ Methods:
+ apply_image: Applies image transformations to labels.
+ apply_instances: Applies transformations to object instances in labels.
+ apply_semantic: Applies semantic segmentation to an image.
+ __call__: Applies all label transformations to an image, instances, and semantic masks.
+
+ Examples:
+ >>> transform = BaseTransform()
+ >>> labels = {"image": np.array(...), "instances": [...], "semantic": np.array(...)}
+ >>> transformed_labels = transform(labels)
+ """
+
+ def __init__(self) -> None:
+ """
+ Initializes the BaseTransform object.
+
+ This constructor sets up the base transformation object, which can be extended for specific image
+ processing tasks. It is designed to be compatible with both classification and semantic segmentation.
+
+ Examples:
+ >>> transform = BaseTransform()
+ """
+ pass
+
+ def apply_image(self, labels):
+ """
+ Applies image transformations to labels.
+
+ This method is intended to be overridden by subclasses to implement specific image transformation
+ logic. In its base form, it returns the input labels unchanged.
+
+ Args:
+ labels (Any): The input labels to be transformed. The exact type and structure of labels may
+ vary depending on the specific implementation.
+
+ Returns:
+ (Any): The transformed labels. In the base implementation, this is identical to the input.
+
+ Examples:
+ >>> transform = BaseTransform()
+ >>> original_labels = [1, 2, 3]
+ >>> transformed_labels = transform.apply_image(original_labels)
+ >>> print(transformed_labels)
+ [1, 2, 3]
+ """
+ pass
+
+ def apply_instances(self, labels):
+ """
+ Applies transformations to object instances in labels.
+
+ This method is responsible for applying various transformations to object instances within the given
+ labels. It is designed to be overridden by subclasses to implement specific instance transformation
+ logic.
+
+ Args:
+ labels (Dict): A dictionary containing label information, including object instances.
+
+ Returns:
+ (Dict): The modified labels dictionary with transformed object instances.
+
+ Examples:
+ >>> transform = BaseTransform()
+ >>> labels = {"instances": Instances(xyxy=torch.rand(5, 4), cls=torch.randint(0, 80, (5,)))}
+ >>> transformed_labels = transform.apply_instances(labels)
+ """
+ pass
+
+ def apply_semantic(self, labels):
+ """
+ Applies semantic segmentation transformations to an image.
+
+ This method is intended to be overridden by subclasses to implement specific semantic segmentation
+ transformations. In its base form, it does not perform any operations.
+
+ Args:
+ labels (Any): The input labels or semantic segmentation mask to be transformed.
+
+ Returns:
+ (Any): The transformed semantic segmentation mask or labels.
+
+ Examples:
+ >>> transform = BaseTransform()
+ >>> semantic_mask = np.zeros((100, 100), dtype=np.uint8)
+ >>> transformed_mask = transform.apply_semantic(semantic_mask)
+ """
+ pass
+
+ def __call__(self, labels):
+ """
+ Applies all label transformations to an image, instances, and semantic masks.
+
+ This method orchestrates the application of various transformations defined in the BaseTransform class
+ to the input labels. It sequentially calls the apply_image and apply_instances methods to process the
+ image and object instances, respectively.
+
+ Args:
+ labels (Dict): A dictionary containing image data and annotations. Expected keys include 'img' for
+ the image data, and 'instances' for object instances.
+
+ Returns:
+ (Dict): The input labels dictionary with transformed image and instances.
+
+ Examples:
+ >>> transform = BaseTransform()
+ >>> labels = {"img": np.random.rand(640, 640, 3), "instances": []}
+ >>> transformed_labels = transform(labels)
+ """
+ self.apply_image(labels)
+ self.apply_instances(labels)
+ self.apply_semantic(labels)
+
+
+class Compose:
+ """
+ A class for composing multiple image transformations.
+
+ Attributes:
+ transforms (List[Callable]): A list of transformation functions to be applied sequentially.
+
+ Methods:
+ __call__: Applies a series of transformations to input data.
+ append: Appends a new transform to the existing list of transforms.
+ insert: Inserts a new transform at a specified index in the list of transforms.
+ __getitem__: Retrieves a specific transform or a set of transforms using indexing.
+ __setitem__: Sets a specific transform or a set of transforms using indexing.
+ tolist: Converts the list of transforms to a standard Python list.
+
+ Examples:
+ >>> transforms = [RandomFlip(), RandomPerspective(30)]
+ >>> compose = Compose(transforms)
+ >>> transformed_data = compose(data)
+ >>> compose.append(CenterCrop((224, 224)))
+ >>> compose.insert(0, RandomFlip())
+ """
+
+ def __init__(self, transforms):
+ """
+ Initializes the Compose object with a list of transforms.
+
+ Args:
+ transforms (List[Callable]): A list of callable transform objects to be applied sequentially.
+
+ Examples:
+ >>> from ultralytics.data.augment import Compose, RandomHSV, RandomFlip
+ >>> transforms = [RandomHSV(), RandomFlip()]
+ >>> compose = Compose(transforms)
+ """
+ self.transforms = transforms if isinstance(transforms, list) else [transforms]
+
+ def __call__(self, data):
+ """
+ Applies a series of transformations to input data. This method sequentially applies each transformation in the
+ Compose object's list of transforms to the input data.
+
+ Args:
+ data (Any): The input data to be transformed. This can be of any type, depending on the
+ transformations in the list.
+
+ Returns:
+ (Any): The transformed data after applying all transformations in sequence.
+
+ Examples:
+ >>> transforms = [Transform1(), Transform2(), Transform3()]
+ >>> compose = Compose(transforms)
+ >>> transformed_data = compose(input_data)
+ """
+ for t in self.transforms:
+ data = t(data)
+ return data
+
+ def append(self, transform):
+ """
+ Appends a new transform to the existing list of transforms.
+
+ Args:
+ transform (BaseTransform): The transformation to be added to the composition.
+
+ Examples:
+ >>> compose = Compose([RandomFlip(), RandomPerspective()])
+ >>> compose.append(RandomHSV())
+ """
+ self.transforms.append(transform)
+
+ def insert(self, index, transform):
+ """
+ Inserts a new transform at a specified index in the existing list of transforms.
+
+ Args:
+ index (int): The index at which to insert the new transform.
+ transform (BaseTransform): The transform object to be inserted.
+
+ Examples:
+ >>> compose = Compose([Transform1(), Transform2()])
+ >>> compose.insert(1, Transform3())
+ >>> len(compose.transforms)
+ 3
+ """
+ self.transforms.insert(index, transform)
+
+ def __getitem__(self, index: Union[list, int]) -> "Compose":
+ """
+ Retrieves a specific transform or a set of transforms using indexing.
+
+ Args:
+ index (int | List[int]): Index or list of indices of the transforms to retrieve.
+
+ Returns:
+ (Compose): A new Compose object containing the selected transform(s).
+
+ Raises:
+ AssertionError: If the index is not of type int or list.
+
+ Examples:
+ >>> transforms = [RandomFlip(), RandomPerspective(10), RandomHSV(0.5, 0.5, 0.5)]
+ >>> compose = Compose(transforms)
+ >>> single_transform = compose[1] # Returns a Compose object with only RandomPerspective
+ >>> multiple_transforms = compose[0:2] # Returns a Compose object with RandomFlip and RandomPerspective
+ """
+ assert isinstance(index, (int, list)), f"The indices should be either list or int type but got {type(index)}"
+ index = [index] if isinstance(index, int) else index
+ return Compose([self.transforms[i] for i in index])
+
+ def __setitem__(self, index: Union[list, int], value: Union[list, int]) -> None:
+ """
+ Sets one or more transforms in the composition using indexing.
+
+ Args:
+ index (int | List[int]): Index or list of indices to set transforms at.
+ value (Any | List[Any]): Transform or list of transforms to set at the specified index(es).
+
+ Raises:
+ AssertionError: If index type is invalid, value type doesn't match index type, or index is out of range.
+
+ Examples:
+ >>> compose = Compose([Transform1(), Transform2(), Transform3()])
+ >>> compose[1] = NewTransform() # Replace second transform
+ >>> compose[0:2] = [NewTransform1(), NewTransform2()] # Replace first two transforms
+ """
+ assert isinstance(index, (int, list)), f"The indices should be either list or int type but got {type(index)}"
+ if isinstance(index, list):
+ assert isinstance(value, list), (
+ f"The indices should be the same type as values, but got {type(index)} and {type(value)}"
+ )
+ if isinstance(index, int):
+ index, value = [index], [value]
+ for i, v in zip(index, value):
+ assert i < len(self.transforms), f"list index {i} out of range {len(self.transforms)}."
+ self.transforms[i] = v
+
+ def tolist(self):
+ """
+ Converts the list of transforms to a standard Python list.
+
+ Returns:
+ (List): A list containing all the transform objects in the Compose instance.
+
+ Examples:
+ >>> transforms = [RandomFlip(), RandomPerspective(10), CenterCrop()]
+ >>> compose = Compose(transforms)
+ >>> transform_list = compose.tolist()
+ >>> print(len(transform_list))
+ 3
+ """
+ return self.transforms
+
+ def __repr__(self):
+ """
+ Returns a string representation of the Compose object.
+
+ Returns:
+ (str): A string representation of the Compose object, including the list of transforms.
+
+ Examples:
+ >>> transforms = [RandomFlip(), RandomPerspective(degrees=10, translate=0.1, scale=0.1)]
+ >>> compose = Compose(transforms)
+ >>> print(compose)
+ Compose([
+ RandomFlip(),
+ RandomPerspective(degrees=10, translate=0.1, scale=0.1)
+ ])
+ """
+ return f"{self.__class__.__name__}({', '.join([f'{t}' for t in self.transforms])})"
+
+
+class BaseMixTransform:
+ """
+ Base class for mix transformations like MixUp and Mosaic.
+
+ This class provides a foundation for implementing mix transformations on datasets. It handles the
+ probability-based application of transforms and manages the mixing of multiple images and labels.
+
+ Attributes:
+ dataset (Any): The dataset object containing images and labels.
+ pre_transform (Callable | None): Optional transform to apply before mixing.
+ p (float): Probability of applying the mix transformation.
+
+ Methods:
+ __call__: Applies the mix transformation to the input labels.
+ _mix_transform: Abstract method to be implemented by subclasses for specific mix operations.
+ get_indexes: Abstract method to get indexes of images to be mixed.
+ _update_label_text: Updates label text for mixed images.
+
+ Examples:
+ >>> class CustomMixTransform(BaseMixTransform):
+ ... def _mix_transform(self, labels):
+ ... # Implement custom mix logic here
+ ... return labels
+ ...
+ ... def get_indexes(self):
+ ... return [random.randint(0, len(self.dataset) - 1) for _ in range(3)]
+ >>> dataset = YourDataset()
+ >>> transform = CustomMixTransform(dataset, p=0.5)
+ >>> mixed_labels = transform(original_labels)
+ """
+
+ def __init__(self, dataset, pre_transform=None, p=0.0) -> None:
+ """
+ Initializes the BaseMixTransform object for mix transformations like MixUp and Mosaic.
+
+ This class serves as a base for implementing mix transformations in image processing pipelines.
+
+ Args:
+ dataset (Any): The dataset object containing images and labels for mixing.
+ pre_transform (Callable | None): Optional transform to apply before mixing.
+ p (float): Probability of applying the mix transformation. Should be in the range [0.0, 1.0].
+
+ Examples:
+ >>> dataset = YOLODataset("path/to/data")
+ >>> pre_transform = Compose([RandomFlip(), RandomPerspective()])
+ >>> mix_transform = BaseMixTransform(dataset, pre_transform, p=0.5)
+ """
+ self.dataset = dataset
+ self.pre_transform = pre_transform
+ self.p = p
+
+ def __call__(self, labels):
+ """
+ Applies pre-processing transforms and mixup/mosaic transforms to labels data.
+
+ This method determines whether to apply the mix transform based on a probability factor. If applied, it
+ selects additional images, applies pre-transforms if specified, and then performs the mix transform.
+
+ Args:
+ labels (Dict): A dictionary containing label data for an image.
+
+ Returns:
+ (Dict): The transformed labels dictionary, which may include mixed data from other images.
+
+ Examples:
+ >>> transform = BaseMixTransform(dataset, pre_transform=None, p=0.5)
+ >>> result = transform({"image": img, "bboxes": boxes, "cls": classes})
+ """
+ if random.uniform(0, 1) > self.p:
+ return labels
+
+ # Get index of one or three other images
+ indexes = self.get_indexes()
+ if isinstance(indexes, int):
+ indexes = [indexes]
+
+ # Get images information will be used for Mosaic or MixUp
+ mix_labels = [self.dataset.get_image_and_label(i) for i in indexes]
+
+ if self.pre_transform is not None:
+ for i, data in enumerate(mix_labels):
+ mix_labels[i] = self.pre_transform(data)
+ labels["mix_labels"] = mix_labels
+
+ # Update cls and texts
+ labels = self._update_label_text(labels)
+ # Mosaic or MixUp
+ labels = self._mix_transform(labels)
+ labels.pop("mix_labels", None)
+ return labels
+
+ def _mix_transform(self, labels):
+ """
+ Applies MixUp or Mosaic augmentation to the label dictionary.
+
+ This method should be implemented by subclasses to perform specific mix transformations like MixUp or
+ Mosaic. It modifies the input label dictionary in-place with the augmented data.
+
+ Args:
+ labels (Dict): A dictionary containing image and label data. Expected to have a 'mix_labels' key
+ with a list of additional image and label data for mixing.
+
+ Returns:
+ (Dict): The modified labels dictionary with augmented data after applying the mix transform.
+
+ Examples:
+ >>> transform = BaseMixTransform(dataset)
+ >>> labels = {"image": img, "bboxes": boxes, "mix_labels": [{"image": img2, "bboxes": boxes2}]}
+ >>> augmented_labels = transform._mix_transform(labels)
+ """
+ raise NotImplementedError
+
+ def get_indexes(self):
+ """
+ Gets a list of shuffled indexes for mosaic augmentation.
+
+ Returns:
+ (List[int]): A list of shuffled indexes from the dataset.
+
+ Examples:
+ >>> transform = BaseMixTransform(dataset)
+ >>> indexes = transform.get_indexes()
+ >>> print(indexes) # [3, 18, 7, 2]
+ """
+ raise NotImplementedError
+
+ @staticmethod
+ def _update_label_text(labels):
+ """
+ Updates label text and class IDs for mixed labels in image augmentation.
+
+ This method processes the 'texts' and 'cls' fields of the input labels dictionary and any mixed labels,
+ creating a unified set of text labels and updating class IDs accordingly.
+
+ Args:
+ labels (Dict): A dictionary containing label information, including 'texts' and 'cls' fields,
+ and optionally a 'mix_labels' field with additional label dictionaries.
+
+ Returns:
+ (Dict): The updated labels dictionary with unified text labels and updated class IDs.
+
+ Examples:
+ >>> labels = {
+ ... "texts": [["cat"], ["dog"]],
+ ... "cls": torch.tensor([[0], [1]]),
+ ... "mix_labels": [{"texts": [["bird"], ["fish"]], "cls": torch.tensor([[0], [1]])}],
+ ... }
+ >>> updated_labels = self._update_label_text(labels)
+ >>> print(updated_labels["texts"])
+ [['cat'], ['dog'], ['bird'], ['fish']]
+ >>> print(updated_labels["cls"])
+ tensor([[0],
+ [1]])
+ >>> print(updated_labels["mix_labels"][0]["cls"])
+ tensor([[2],
+ [3]])
+ """
+ if "texts" not in labels:
+ return labels
+
+ mix_texts = sum([labels["texts"]] + [x["texts"] for x in labels["mix_labels"]], [])
+ mix_texts = list({tuple(x) for x in mix_texts})
+ text2id = {text: i for i, text in enumerate(mix_texts)}
+
+ for label in [labels] + labels["mix_labels"]:
+ for i, cls in enumerate(label["cls"].squeeze(-1).tolist()):
+ text = label["texts"][int(cls)]
+ label["cls"][i] = text2id[tuple(text)]
+ label["texts"] = mix_texts
+ return labels
+
+
+class Mosaic(BaseMixTransform):
+ """
+ Mosaic augmentation for image datasets.
+
+ This class performs mosaic augmentation by combining multiple (4 or 9) images into a single mosaic image.
+ The augmentation is applied to a dataset with a given probability.
+
+ Attributes:
+ dataset: The dataset on which the mosaic augmentation is applied.
+ imgsz (int): Image size (height and width) after mosaic pipeline of a single image.
+ p (float): Probability of applying the mosaic augmentation. Must be in the range 0-1.
+ n (int): The grid size, either 4 (for 2x2) or 9 (for 3x3).
+ border (Tuple[int, int]): Border size for width and height.
+
+ Methods:
+ get_indexes: Returns a list of random indexes from the dataset.
+ _mix_transform: Applies mixup transformation to the input image and labels.
+ _mosaic3: Creates a 1x3 image mosaic.
+ _mosaic4: Creates a 2x2 image mosaic.
+ _mosaic9: Creates a 3x3 image mosaic.
+ _update_labels: Updates labels with padding.
+ _cat_labels: Concatenates labels and clips mosaic border instances.
+
+ Examples:
+ >>> from ultralytics.data.augment import Mosaic
+ >>> dataset = YourDataset(...) # Your image dataset
+ >>> mosaic_aug = Mosaic(dataset, imgsz=640, p=0.5, n=4)
+ >>> augmented_labels = mosaic_aug(original_labels)
+ """
+
+ def __init__(self, dataset, imgsz=640, p=1.0, n=4):
+ """
+ Initializes the Mosaic augmentation object.
+
+ This class performs mosaic augmentation by combining multiple (4 or 9) images into a single mosaic image.
+ The augmentation is applied to a dataset with a given probability.
+
+ Args:
+ dataset (Any): The dataset on which the mosaic augmentation is applied.
+ imgsz (int): Image size (height and width) after mosaic pipeline of a single image.
+ p (float): Probability of applying the mosaic augmentation. Must be in the range 0-1.
+ n (int): The grid size, either 4 (for 2x2) or 9 (for 3x3).
+
+ Examples:
+ >>> from ultralytics.data.augment import Mosaic
+ >>> dataset = YourDataset(...)
+ >>> mosaic_aug = Mosaic(dataset, imgsz=640, p=0.5, n=4)
+ """
+ assert 0 <= p <= 1.0, f"The probability should be in range [0, 1], but got {p}."
+ assert n in {4, 9}, "grid must be equal to 4 or 9."
+ super().__init__(dataset=dataset, p=p)
+ self.imgsz = imgsz
+ self.border = (-imgsz // 2, -imgsz // 2) # width, height
+ self.n = n
+
+ def get_indexes(self, buffer=True):
+ """
+ Returns a list of random indexes from the dataset for mosaic augmentation.
+
+ This method selects random image indexes either from a buffer or from the entire dataset, depending on
+ the 'buffer' parameter. It is used to choose images for creating mosaic augmentations.
+
+ Args:
+ buffer (bool): If True, selects images from the dataset buffer. If False, selects from the entire
+ dataset.
+
+ Returns:
+ (List[int]): A list of random image indexes. The length of the list is n-1, where n is the number
+ of images used in the mosaic (either 3 or 8, depending on whether n is 4 or 9).
+
+ Examples:
+ >>> mosaic = Mosaic(dataset, imgsz=640, p=1.0, n=4)
+ >>> indexes = mosaic.get_indexes()
+ >>> print(len(indexes)) # Output: 3
+ """
+ if buffer: # select images from buffer
+ return random.choices(list(self.dataset.buffer), k=self.n - 1)
+ else: # select any images
+ return [random.randint(0, len(self.dataset) - 1) for _ in range(self.n - 1)]
+
+ def _mix_transform(self, labels):
+ """
+ Applies mosaic augmentation to the input image and labels.
+
+ This method combines multiple images (3, 4, or 9) into a single mosaic image based on the 'n' attribute.
+ It ensures that rectangular annotations are not present and that there are other images available for
+ mosaic augmentation.
+
+ Args:
+ labels (Dict): A dictionary containing image data and annotations. Expected keys include:
+ - 'rect_shape': Should be None as rect and mosaic are mutually exclusive.
+ - 'mix_labels': A list of dictionaries containing data for other images to be used in the mosaic.
+
+ Returns:
+ (Dict): A dictionary containing the mosaic-augmented image and updated annotations.
+
+ Raises:
+ AssertionError: If 'rect_shape' is not None or if 'mix_labels' is empty.
+
+ Examples:
+ >>> mosaic = Mosaic(dataset, imgsz=640, p=1.0, n=4)
+ >>> augmented_data = mosaic._mix_transform(labels)
+ """
+ assert labels.get("rect_shape", None) is None, "rect and mosaic are mutually exclusive."
+ assert len(labels.get("mix_labels", [])), "There are no other images for mosaic augment."
+ return (
+ self._mosaic3(labels) if self.n == 3 else self._mosaic4(labels) if self.n == 4 else self._mosaic9(labels)
+ ) # This code is modified for mosaic3 method.
+
+ def _mosaic3(self, labels):
+ """
+ Creates a 1x3 image mosaic by combining three images.
+
+ This method arranges three images in a horizontal layout, with the main image in the center and two
+ additional images on either side. It's part of the Mosaic augmentation technique used in object detection.
+
+ Args:
+ labels (Dict): A dictionary containing image and label information for the main (center) image.
+ Must include 'img' key with the image array, and 'mix_labels' key with a list of two
+ dictionaries containing information for the side images.
+
+ Returns:
+ (Dict): A dictionary with the mosaic image and updated labels. Keys include:
+ - 'img' (np.ndarray): The mosaic image array with shape (H, W, C).
+ - Other keys from the input labels, updated to reflect the new image dimensions.
+
+ Examples:
+ >>> mosaic = Mosaic(dataset, imgsz=640, p=1.0, n=3)
+ >>> labels = {
+ ... "img": np.random.rand(480, 640, 3),
+ ... "mix_labels": [{"img": np.random.rand(480, 640, 3)} for _ in range(2)],
+ ... }
+ >>> result = mosaic._mosaic3(labels)
+ >>> print(result["img"].shape)
+ (640, 640, 3)
+ """
+ mosaic_labels = []
+ s = self.imgsz
+ for i in range(3):
+ labels_patch = labels if i == 0 else labels["mix_labels"][i - 1]
+ # Load image
+ img = labels_patch["img"]
+ h, w = labels_patch.pop("resized_shape")
+
+ # Place img in img3
+ if i == 0: # center
+ img3 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 3 tiles
+ h0, w0 = h, w
+ c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates
+ elif i == 1: # right
+ c = s + w0, s, s + w0 + w, s + h
+ elif i == 2: # left
+ c = s - w, s + h0 - h, s, s + h0
+
+ padw, padh = c[:2]
+ x1, y1, x2, y2 = (max(x, 0) for x in c) # allocate coordinates
+
+ img3[y1:y2, x1:x2] = img[y1 - padh :, x1 - padw :] # img3[ymin:ymax, xmin:xmax]
+ # hp, wp = h, w # height, width previous for next iteration
+
+ # Labels assuming imgsz*2 mosaic size
+ labels_patch = self._update_labels(labels_patch, padw + self.border[0], padh + self.border[1])
+ mosaic_labels.append(labels_patch)
+ final_labels = self._cat_labels(mosaic_labels)
+
+ final_labels["img"] = img3[-self.border[0] : self.border[0], -self.border[1] : self.border[1]]
+ return final_labels
+
+ def _mosaic4(self, labels):
+ """
+ Creates a 2x2 image mosaic from four input images.
+
+ This method combines four images into a single mosaic image by placing them in a 2x2 grid. It also
+ updates the corresponding labels for each image in the mosaic.
+
+ Args:
+ labels (Dict): A dictionary containing image data and labels for the base image (index 0) and three
+ additional images (indices 1-3) in the 'mix_labels' key.
+
+ Returns:
+ (Dict): A dictionary containing the mosaic image and updated labels. The 'img' key contains the mosaic
+ image as a numpy array, and other keys contain the combined and adjusted labels for all four images.
+
+ Examples:
+ >>> mosaic = Mosaic(dataset, imgsz=640, p=1.0, n=4)
+ >>> labels = {
+ ... "img": np.random.rand(480, 640, 3),
+ ... "mix_labels": [{"img": np.random.rand(480, 640, 3)} for _ in range(3)],
+ ... }
+ >>> result = mosaic._mosaic4(labels)
+ >>> assert result["img"].shape == (1280, 1280, 3)
+ """
+ mosaic_labels = []
+ s = self.imgsz
+ yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.border) # mosaic center x, y
+ for i in range(4):
+ labels_patch = labels if i == 0 else labels["mix_labels"][i - 1]
+ # Load image
+ img = labels_patch["img"]
+ h, w = labels_patch.pop("resized_shape")
+
+ # Place img in img4
+ if i == 0: # top left
+ img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
+ x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image)
+ x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image)
+ elif i == 1: # top right
+ x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
+ x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
+ elif i == 2: # bottom left
+ x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
+ x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
+ elif i == 3: # bottom right
+ x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
+ x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
+
+ img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax]
+ padw = x1a - x1b
+ padh = y1a - y1b
+
+ labels_patch = self._update_labels(labels_patch, padw, padh)
+ mosaic_labels.append(labels_patch)
+ final_labels = self._cat_labels(mosaic_labels)
+ final_labels["img"] = img4
+ return final_labels
+
+ def _mosaic9(self, labels):
+ """
+ Creates a 3x3 image mosaic from the input image and eight additional images.
+
+ This method combines nine images into a single mosaic image. The input image is placed at the center,
+ and eight additional images from the dataset are placed around it in a 3x3 grid pattern.
+
+ Args:
+ labels (Dict): A dictionary containing the input image and its associated labels. It should have
+ the following keys:
+ - 'img' (numpy.ndarray): The input image.
+ - 'resized_shape' (Tuple[int, int]): The shape of the resized image (height, width).
+ - 'mix_labels' (List[Dict]): A list of dictionaries containing information for the additional
+ eight images, each with the same structure as the input labels.
+
+ Returns:
+ (Dict): A dictionary containing the mosaic image and updated labels. It includes the following keys:
+ - 'img' (numpy.ndarray): The final mosaic image.
+ - Other keys from the input labels, updated to reflect the new mosaic arrangement.
+
+ Examples:
+ >>> mosaic = Mosaic(dataset, imgsz=640, p=1.0, n=9)
+ >>> input_labels = dataset[0]
+ >>> mosaic_result = mosaic._mosaic9(input_labels)
+ >>> mosaic_image = mosaic_result["img"]
+ """
+ mosaic_labels = []
+ s = self.imgsz
+ hp, wp = -1, -1 # height, width previous
+ for i in range(9):
+ labels_patch = labels if i == 0 else labels["mix_labels"][i - 1]
+ # Load image
+ img = labels_patch["img"]
+ h, w = labels_patch.pop("resized_shape")
+
+ # Place img in img9
+ if i == 0: # center
+ img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
+ h0, w0 = h, w
+ c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates
+ elif i == 1: # top
+ c = s, s - h, s + w, s
+ elif i == 2: # top right
+ c = s + wp, s - h, s + wp + w, s
+ elif i == 3: # right
+ c = s + w0, s, s + w0 + w, s + h
+ elif i == 4: # bottom right
+ c = s + w0, s + hp, s + w0 + w, s + hp + h
+ elif i == 5: # bottom
+ c = s + w0 - w, s + h0, s + w0, s + h0 + h
+ elif i == 6: # bottom left
+ c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h
+ elif i == 7: # left
+ c = s - w, s + h0 - h, s, s + h0
+ elif i == 8: # top left
+ c = s - w, s + h0 - hp - h, s, s + h0 - hp
+
+ padw, padh = c[:2]
+ x1, y1, x2, y2 = (max(x, 0) for x in c) # allocate coordinates
+
+ # Image
+ img9[y1:y2, x1:x2] = img[y1 - padh :, x1 - padw :] # img9[ymin:ymax, xmin:xmax]
+ hp, wp = h, w # height, width previous for next iteration
+
+ # Labels assuming imgsz*2 mosaic size
+ labels_patch = self._update_labels(labels_patch, padw + self.border[0], padh + self.border[1])
+ mosaic_labels.append(labels_patch)
+ final_labels = self._cat_labels(mosaic_labels)
+
+ final_labels["img"] = img9[-self.border[0] : self.border[0], -self.border[1] : self.border[1]]
+ return final_labels
+
+ @staticmethod
+ def _update_labels(labels, padw, padh):
+ """
+ Updates label coordinates with padding values.
+
+ This method adjusts the bounding box coordinates of object instances in the labels by adding padding
+ values. It also denormalizes the coordinates if they were previously normalized.
+
+ Args:
+ labels (Dict): A dictionary containing image and instance information.
+ padw (int): Padding width to be added to the x-coordinates.
+ padh (int): Padding height to be added to the y-coordinates.
+
+ Returns:
+ (Dict): Updated labels dictionary with adjusted instance coordinates.
+
+ Examples:
+ >>> labels = {"img": np.zeros((100, 100, 3)), "instances": Instances(...)}
+ >>> padw, padh = 50, 50
+ >>> updated_labels = Mosaic._update_labels(labels, padw, padh)
+ """
+ nh, nw = labels["img"].shape[:2]
+ labels["instances"].convert_bbox(format="xyxy")
+ labels["instances"].denormalize(nw, nh)
+ labels["instances"].add_padding(padw, padh)
+ return labels
+
+ def _cat_labels(self, mosaic_labels):
+ """
+ Concatenates and processes labels for mosaic augmentation.
+
+ This method combines labels from multiple images used in mosaic augmentation, clips instances to the
+ mosaic border, and removes zero-area boxes.
+
+ Args:
+ mosaic_labels (List[Dict]): A list of label dictionaries for each image in the mosaic.
+
+ Returns:
+ (Dict): A dictionary containing concatenated and processed labels for the mosaic image, including:
+ - im_file (str): File path of the first image in the mosaic.
+ - ori_shape (Tuple[int, int]): Original shape of the first image.
+ - resized_shape (Tuple[int, int]): Shape of the mosaic image (imgsz * 2, imgsz * 2).
+ - cls (np.ndarray): Concatenated class labels.
+ - instances (Instances): Concatenated instance annotations.
+ - mosaic_border (Tuple[int, int]): Mosaic border size.
+ - texts (List[str], optional): Text labels if present in the original labels.
+
+ Examples:
+ >>> mosaic = Mosaic(dataset, imgsz=640)
+ >>> mosaic_labels = [{"cls": np.array([0, 1]), "instances": Instances(...)} for _ in range(4)]
+ >>> result = mosaic._cat_labels(mosaic_labels)
+ >>> print(result.keys())
+ dict_keys(['im_file', 'ori_shape', 'resized_shape', 'cls', 'instances', 'mosaic_border'])
+ """
+ if len(mosaic_labels) == 0:
+ return {}
+ cls = []
+ instances = []
+ imgsz = self.imgsz * 2 # mosaic imgsz
+ for labels in mosaic_labels:
+ cls.append(labels["cls"])
+ instances.append(labels["instances"])
+ # Final labels
+ final_labels = {
+ "im_file": mosaic_labels[0]["im_file"],
+ "ori_shape": mosaic_labels[0]["ori_shape"],
+ "resized_shape": (imgsz, imgsz),
+ "cls": np.concatenate(cls, 0),
+ "instances": Instances.concatenate(instances, axis=0),
+ "mosaic_border": self.border,
+ }
+ final_labels["instances"].clip(imgsz, imgsz)
+ good = final_labels["instances"].remove_zero_area_boxes()
+ final_labels["cls"] = final_labels["cls"][good]
+ if "texts" in mosaic_labels[0]:
+ final_labels["texts"] = mosaic_labels[0]["texts"]
+ return final_labels
+
+
+class MixUp(BaseMixTransform):
+ """
+ Applies MixUp augmentation to image datasets.
+
+ This class implements the MixUp augmentation technique as described in the paper "mixup: Beyond Empirical Risk
+ Minimization" (https://arxiv.org/abs/1710.09412). MixUp combines two images and their labels using a random weight.
+
+ Attributes:
+ dataset (Any): The dataset to which MixUp augmentation will be applied.
+ pre_transform (Callable | None): Optional transform to apply before MixUp.
+ p (float): Probability of applying MixUp augmentation.
+
+ Methods:
+ get_indexes: Returns a random index from the dataset.
+ _mix_transform: Applies MixUp augmentation to the input labels.
+
+ Examples:
+ >>> from ultralytics.data.augment import MixUp
+ >>> dataset = YourDataset(...) # Your image dataset
+ >>> mixup = MixUp(dataset, p=0.5)
+ >>> augmented_labels = mixup(original_labels)
+ """
+
+ def __init__(self, dataset, pre_transform=None, p=0.0) -> None:
+ """
+ Initializes the MixUp augmentation object.
+
+ MixUp is an image augmentation technique that combines two images by taking a weighted sum of their pixel
+ values and labels. This implementation is designed for use with the Ultralytics YOLO framework.
+
+ Args:
+ dataset (Any): The dataset to which MixUp augmentation will be applied.
+ pre_transform (Callable | None): Optional transform to apply to images before MixUp.
+ p (float): Probability of applying MixUp augmentation to an image. Must be in the range [0, 1].
+
+ Examples:
+ >>> from ultralytics.data.dataset import YOLODataset
+ >>> dataset = YOLODataset("path/to/data.yaml")
+ >>> mixup = MixUp(dataset, pre_transform=None, p=0.5)
+ """
+ super().__init__(dataset=dataset, pre_transform=pre_transform, p=p)
+
+ def get_indexes(self):
+ """
+ Get a random index from the dataset.
+
+ This method returns a single random index from the dataset, which is used to select an image for MixUp
+ augmentation.
+
+ Returns:
+ (int): A random integer index within the range of the dataset length.
+
+ Examples:
+ >>> mixup = MixUp(dataset)
+ >>> index = mixup.get_indexes()
+ >>> print(index)
+ 42
+ """
+ return random.randint(0, len(self.dataset) - 1)
+
+ def _mix_transform(self, labels):
+ """
+ Applies MixUp augmentation to the input labels.
+
+ This method implements the MixUp augmentation technique as described in the paper
+ "mixup: Beyond Empirical Risk Minimization" (https://arxiv.org/abs/1710.09412).
+
+ Args:
+ labels (Dict): A dictionary containing the original image and label information.
+
+ Returns:
+ (Dict): A dictionary containing the mixed-up image and combined label information.
+
+ Examples:
+ >>> mixer = MixUp(dataset)
+ >>> mixed_labels = mixer._mix_transform(labels)
+ """
+ r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
+ labels2 = labels["mix_labels"][0]
+ labels["img"] = (labels["img"] * r + labels2["img"] * (1 - r)).astype(np.uint8)
+ labels["instances"] = Instances.concatenate([labels["instances"], labels2["instances"]], axis=0)
+ labels["cls"] = np.concatenate([labels["cls"], labels2["cls"]], 0)
+ return labels
+
+
+class RandomPerspective:
+ """
+ Implements random perspective and affine transformations on images and corresponding annotations.
+
+ This class applies random rotations, translations, scaling, shearing, and perspective transformations
+ to images and their associated bounding boxes, segments, and keypoints. It can be used as part of an
+ augmentation pipeline for object detection and instance segmentation tasks.
+
+ Attributes:
+ degrees (float): Maximum absolute degree range for random rotations.
+ translate (float): Maximum translation as a fraction of the image size.
+ scale (float): Scaling factor range, e.g., scale=0.1 means 0.9-1.1.
+ shear (float): Maximum shear angle in degrees.
+ perspective (float): Perspective distortion factor.
+ border (Tuple[int, int]): Mosaic border size as (x, y).
+ pre_transform (Callable | None): Optional transform to apply before the random perspective.
+
+ Methods:
+ affine_transform: Applies affine transformations to the input image.
+ apply_bboxes: Transforms bounding boxes using the affine matrix.
+ apply_segments: Transforms segments and generates new bounding boxes.
+ apply_keypoints: Transforms keypoints using the affine matrix.
+ __call__: Applies the random perspective transformation to images and annotations.
+ box_candidates: Filters transformed bounding boxes based on size and aspect ratio.
+
+ Examples:
+ >>> transform = RandomPerspective(degrees=10, translate=0.1, scale=0.1, shear=10)
+ >>> image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
+ >>> labels = {"img": image, "cls": np.array([0, 1]), "instances": Instances(...)}
+ >>> result = transform(labels)
+ >>> transformed_image = result["img"]
+ >>> transformed_instances = result["instances"]
+ """
+
+ def __init__(
+ self, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, border=(0, 0), pre_transform=None
+ ):
+ """
+ Initializes RandomPerspective object with transformation parameters.
+
+ This class implements random perspective and affine transformations on images and corresponding bounding boxes,
+ segments, and keypoints. Transformations include rotation, translation, scaling, and shearing.
+
+ Args:
+ degrees (float): Degree range for random rotations.
+ translate (float): Fraction of total width and height for random translation.
+ scale (float): Scaling factor interval, e.g., a scale factor of 0.5 allows a resize between 50%-150%.
+ shear (float): Shear intensity (angle in degrees).
+ perspective (float): Perspective distortion factor.
+ border (Tuple[int, int]): Tuple specifying mosaic border (top/bottom, left/right).
+ pre_transform (Callable | None): Function/transform to apply to the image before starting the random
+ transformation.
+
+ Examples:
+ >>> transform = RandomPerspective(degrees=10.0, translate=0.1, scale=0.5, shear=5.0)
+ >>> result = transform(labels) # Apply random perspective to labels
+ """
+ self.degrees = degrees
+ self.translate = translate
+ self.scale = scale
+ self.shear = shear
+ self.perspective = perspective
+ self.border = border # mosaic border
+ self.pre_transform = pre_transform
+
+ def affine_transform(self, img, border):
+ """
+ Applies a sequence of affine transformations centered around the image center.
+
+ This function performs a series of geometric transformations on the input image, including
+ translation, perspective change, rotation, scaling, and shearing. The transformations are
+ applied in a specific order to maintain consistency.
+
+ Args:
+ img (np.ndarray): Input image to be transformed.
+ border (Tuple[int, int]): Border dimensions for the transformed image.
+
+ Returns:
+ (Tuple[np.ndarray, np.ndarray, float]): A tuple containing:
+ - np.ndarray: Transformed image.
+ - np.ndarray: 3x3 transformation matrix.
+ - float: Scale factor applied during the transformation.
+
+ Examples:
+ >>> import numpy as np
+ >>> img = np.random.rand(100, 100, 3)
+ >>> border = (10, 10)
+ >>> transformed_img, matrix, scale = affine_transform(img, border)
+ """
+ # Center
+ C = np.eye(3, dtype=np.float32)
+
+ C[0, 2] = -img.shape[1] / 2 # x translation (pixels)
+ C[1, 2] = -img.shape[0] / 2 # y translation (pixels)
+
+ # Perspective
+ P = np.eye(3, dtype=np.float32)
+ P[2, 0] = random.uniform(-self.perspective, self.perspective) # x perspective (about y)
+ P[2, 1] = random.uniform(-self.perspective, self.perspective) # y perspective (about x)
+
+ # Rotation and Scale
+ R = np.eye(3, dtype=np.float32)
+ a = random.uniform(-self.degrees, self.degrees)
+ # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
+ s = random.uniform(1 - self.scale, 1 + self.scale)
+ # s = 2 ** random.uniform(-scale, scale)
+ R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
+
+ # Shear
+ S = np.eye(3, dtype=np.float32)
+ S[0, 1] = math.tan(random.uniform(-self.shear, self.shear) * math.pi / 180) # x shear (deg)
+ S[1, 0] = math.tan(random.uniform(-self.shear, self.shear) * math.pi / 180) # y shear (deg)
+
+ # Translation
+ T = np.eye(3, dtype=np.float32)
+ T[0, 2] = random.uniform(0.5 - self.translate, 0.5 + self.translate) * self.size[0] # x translation (pixels)
+ T[1, 2] = random.uniform(0.5 - self.translate, 0.5 + self.translate) * self.size[1] # y translation (pixels)
+
+ # Combined rotation matrix
+ M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
+ # Affine image
+ if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
+ if self.perspective:
+ img = cv2.warpPerspective(img, M, dsize=self.size, borderValue=(114, 114, 114))
+ else: # affine
+ img = cv2.warpAffine(img, M[:2], dsize=self.size, borderValue=(114, 114, 114))
+ return img, M, s
+
+ def apply_bboxes(self, bboxes, M):
+ """
+ Apply affine transformation to bounding boxes.
+
+ This function applies an affine transformation to a set of bounding boxes using the provided
+ transformation matrix.
+
+ Args:
+ bboxes (torch.Tensor): Bounding boxes in xyxy format with shape (N, 4), where N is the number
+ of bounding boxes.
+ M (torch.Tensor): Affine transformation matrix with shape (3, 3).
+
+ Returns:
+ (torch.Tensor): Transformed bounding boxes in xyxy format with shape (N, 4).
+
+ Examples:
+ >>> bboxes = torch.tensor([[10, 10, 20, 20], [30, 30, 40, 40]])
+ >>> M = torch.eye(3)
+ >>> transformed_bboxes = apply_bboxes(bboxes, M)
+ """
+ n = len(bboxes)
+ if n == 0:
+ return bboxes
+
+ xy = np.ones((n * 4, 3), dtype=bboxes.dtype)
+ xy[:, :2] = bboxes[:, [0, 1, 2, 3, 0, 3, 2, 1]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
+ xy = xy @ M.T # transform
+ xy = (xy[:, :2] / xy[:, 2:3] if self.perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine
+
+ # Create new boxes
+ x = xy[:, [0, 2, 4, 6]]
+ y = xy[:, [1, 3, 5, 7]]
+ return np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1)), dtype=bboxes.dtype).reshape(4, n).T
+
+ def apply_segments(self, segments, M):
+ """
+ Apply affine transformations to segments and generate new bounding boxes.
+
+ This function applies affine transformations to input segments and generates new bounding boxes based on
+ the transformed segments. It clips the transformed segments to fit within the new bounding boxes.
+
+ Args:
+ segments (np.ndarray): Input segments with shape (N, M, 2), where N is the number of segments and M is the
+ number of points in each segment.
+ M (np.ndarray): Affine transformation matrix with shape (3, 3).
+
+ Returns:
+ (Tuple[np.ndarray, np.ndarray]): A tuple containing:
+ - New bounding boxes with shape (N, 4) in xyxy format.
+ - Transformed and clipped segments with shape (N, M, 2).
+
+ Examples:
+ >>> segments = np.random.rand(10, 500, 2) # 10 segments with 500 points each
+ >>> M = np.eye(3) # Identity transformation matrix
+ >>> new_bboxes, new_segments = apply_segments(segments, M)
+ """
+ n, num = segments.shape[:2]
+ if n == 0:
+ return [], segments
+
+ xy = np.ones((n * num, 3), dtype=segments.dtype)
+ segments = segments.reshape(-1, 2)
+ xy[:, :2] = segments
+ xy = xy @ M.T # transform
+ xy = xy[:, :2] / xy[:, 2:3]
+ segments = xy.reshape(n, -1, 2)
+ bboxes = np.stack([segment2box(xy, self.size[0], self.size[1]) for xy in segments], 0)
+ segments[..., 0] = segments[..., 0].clip(bboxes[:, 0:1], bboxes[:, 2:3])
+ segments[..., 1] = segments[..., 1].clip(bboxes[:, 1:2], bboxes[:, 3:4])
+ return bboxes, segments
+
+ def apply_keypoints(self, keypoints, M):
+ """
+ Applies affine transformation to keypoints.
+
+ This method transforms the input keypoints using the provided affine transformation matrix. It handles
+ perspective rescaling if necessary and updates the visibility of keypoints that fall outside the image
+ boundaries after transformation.
+
+ Args:
+ keypoints (np.ndarray): Array of keypoints with shape (N, 17, 3), where N is the number of instances,
+ 17 is the number of keypoints per instance, and 3 represents (x, y, visibility).
+ M (np.ndarray): 3x3 affine transformation matrix.
+
+ Returns:
+ (np.ndarray): Transformed keypoints array with the same shape as input (N, 17, 3).
+
+ Examples:
+ >>> random_perspective = RandomPerspective()
+ >>> keypoints = np.random.rand(5, 17, 3) # 5 instances, 17 keypoints each
+ >>> M = np.eye(3) # Identity transformation
+ >>> transformed_keypoints = random_perspective.apply_keypoints(keypoints, M)
+ """
+ n, nkpt = keypoints.shape[:2]
+ if n == 0:
+ return keypoints
+ xy = np.ones((n * nkpt, 3), dtype=keypoints.dtype)
+ visible = keypoints[..., 2].reshape(n * nkpt, 1)
+ xy[:, :2] = keypoints[..., :2].reshape(n * nkpt, 2)
+ xy = xy @ M.T # transform
+ xy = xy[:, :2] / xy[:, 2:3] # perspective rescale or affine
+ out_mask = (xy[:, 0] < 0) | (xy[:, 1] < 0) | (xy[:, 0] > self.size[0]) | (xy[:, 1] > self.size[1])
+ visible[out_mask] = 0
+ return np.concatenate([xy, visible], axis=-1).reshape(n, nkpt, 3)
+
+ def __call__(self, labels):
+ """
+ Applies random perspective and affine transformations to an image and its associated labels.
+
+ This method performs a series of transformations including rotation, translation, scaling, shearing,
+ and perspective distortion on the input image and adjusts the corresponding bounding boxes, segments,
+ and keypoints accordingly.
+
+ Args:
+ labels (Dict): A dictionary containing image data and annotations.
+ Must include:
+ 'img' (ndarray): The input image.
+ 'cls' (ndarray): Class labels.
+ 'instances' (Instances): Object instances with bounding boxes, segments, and keypoints.
+ May include:
+ 'mosaic_border' (Tuple[int, int]): Border size for mosaic augmentation.
+
+ Returns:
+ (Dict): Transformed labels dictionary containing:
+ - 'img' (np.ndarray): The transformed image.
+ - 'cls' (np.ndarray): Updated class labels.
+ - 'instances' (Instances): Updated object instances.
+ - 'resized_shape' (Tuple[int, int]): New image shape after transformation.
+
+ Examples:
+ >>> transform = RandomPerspective()
+ >>> image = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
+ >>> labels = {
+ ... "img": image,
+ ... "cls": np.array([0, 1, 2]),
+ ... "instances": Instances(bboxes=np.array([[10, 10, 50, 50], [100, 100, 150, 150]])),
+ ... }
+ >>> result = transform(labels)
+ >>> assert result["img"].shape[:2] == result["resized_shape"]
+ """
+ if self.pre_transform and "mosaic_border" not in labels:
+ labels = self.pre_transform(labels)
+ labels.pop("ratio_pad", None) # do not need ratio pad
+
+ img = labels["img"]
+ cls = labels["cls"]
+ instances = labels.pop("instances")
+ # Make sure the coord formats are right
+ instances.convert_bbox(format="xyxy")
+ instances.denormalize(*img.shape[:2][::-1])
+
+ border = labels.pop("mosaic_border", self.border)
+ self.size = img.shape[1] + border[1] * 2, img.shape[0] + border[0] * 2 # w, h
+ # M is affine matrix
+ # Scale for func:`box_candidates`
+ img, M, scale = self.affine_transform(img, border)
+
+ bboxes = self.apply_bboxes(instances.bboxes, M)
+
+ segments = instances.segments
+ keypoints = instances.keypoints
+ # Update bboxes if there are segments.
+ if len(segments):
+ bboxes, segments = self.apply_segments(segments, M)
+
+ if keypoints is not None:
+ keypoints = self.apply_keypoints(keypoints, M)
+ new_instances = Instances(bboxes, segments, keypoints, bbox_format="xyxy", normalized=False)
+ # Clip
+ new_instances.clip(*self.size)
+
+ # Filter instances
+ instances.scale(scale_w=scale, scale_h=scale, bbox_only=True)
+ # Make the bboxes have the same scale with new_bboxes
+ i = self.box_candidates(
+ box1=instances.bboxes.T, box2=new_instances.bboxes.T, area_thr=0.01 if len(segments) else 0.10
+ )
+ labels["instances"] = new_instances[i]
+ labels["cls"] = cls[i]
+ labels["img"] = img
+ labels["resized_shape"] = img.shape[:2]
+ return labels
+
+ @staticmethod
+ def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16):
+ """
+ Compute candidate boxes for further processing based on size and aspect ratio criteria.
+
+ This method compares boxes before and after augmentation to determine if they meet specified
+ thresholds for width, height, aspect ratio, and area. It's used to filter out boxes that have
+ been overly distorted or reduced by the augmentation process.
+
+ Args:
+ box1 (numpy.ndarray): Original boxes before augmentation, shape (4, N) where n is the
+ number of boxes. Format is [x1, y1, x2, y2] in absolute coordinates.
+ box2 (numpy.ndarray): Augmented boxes after transformation, shape (4, N). Format is
+ [x1, y1, x2, y2] in absolute coordinates.
+ wh_thr (float): Width and height threshold in pixels. Boxes smaller than this in either
+ dimension are rejected.
+ ar_thr (float): Aspect ratio threshold. Boxes with an aspect ratio greater than this
+ value are rejected.
+ area_thr (float): Area ratio threshold. Boxes with an area ratio (new/old) less than
+ this value are rejected.
+ eps (float): Small epsilon value to prevent division by zero.
+
+ Returns:
+ (numpy.ndarray): Boolean array of shape (n) indicating which boxes are candidates.
+ True values correspond to boxes that meet all criteria.
+
+ Examples:
+ >>> random_perspective = RandomPerspective()
+ >>> box1 = np.array([[0, 0, 100, 100], [0, 0, 50, 50]]).T
+ >>> box2 = np.array([[10, 10, 90, 90], [5, 5, 45, 45]]).T
+ >>> candidates = random_perspective.box_candidates(box1, box2)
+ >>> print(candidates)
+ [True True]
+ """
+ w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
+ w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
+ ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio
+ return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates
+
+
+class RandomHSV:
+ """
+ Randomly adjusts the Hue, Saturation, and Value (HSV) channels of an image.
+
+ This class applies random HSV augmentation to images within predefined limits set by hgain, sgain, and vgain.
+
+ Attributes:
+ hgain (float): Maximum variation for hue. Range is typically [0, 1].
+ sgain (float): Maximum variation for saturation. Range is typically [0, 1].
+ vgain (float): Maximum variation for value. Range is typically [0, 1].
+
+ Methods:
+ __call__: Applies random HSV augmentation to an image.
+
+ Examples:
+ >>> import numpy as np
+ >>> from ultralytics.data.augment import RandomHSV
+ >>> augmenter = RandomHSV(hgain=0.5, sgain=0.5, vgain=0.5)
+ >>> image = np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)
+ >>> labels = {"img": image}
+ >>> augmenter(labels)
+ >>> augmented_image = augmented_labels["img"]
+ """
+
+ def __init__(self, hgain=0.5, sgain=0.5, vgain=0.5) -> None:
+ """
+ Initializes the RandomHSV object for random HSV (Hue, Saturation, Value) augmentation.
+
+ This class applies random adjustments to the HSV channels of an image within specified limits.
+
+ Args:
+ hgain (float): Maximum variation for hue. Should be in the range [0, 1].
+ sgain (float): Maximum variation for saturation. Should be in the range [0, 1].
+ vgain (float): Maximum variation for value. Should be in the range [0, 1].
+
+ Examples:
+ >>> hsv_aug = RandomHSV(hgain=0.5, sgain=0.5, vgain=0.5)
+ >>> hsv_aug(image)
+ """
+ self.hgain = hgain
+ self.sgain = sgain
+ self.vgain = vgain
+
+ def __call__(self, labels):
+ """
+ Applies random HSV augmentation to an image within predefined limits.
+
+ This method modifies the input image by randomly adjusting its Hue, Saturation, and Value (HSV) channels.
+ The adjustments are made within the limits set by hgain, sgain, and vgain during initialization.
+
+ Args:
+ labels (Dict): A dictionary containing image data and metadata. Must include an 'img' key with
+ the image as a numpy array.
+
+ Returns:
+ (None): The function modifies the input 'labels' dictionary in-place, updating the 'img' key
+ with the HSV-augmented image.
+
+ Examples:
+ >>> hsv_augmenter = RandomHSV(hgain=0.5, sgain=0.5, vgain=0.5)
+ >>> labels = {"img": np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)}
+ >>> hsv_augmenter(labels)
+ >>> augmented_img = labels["img"]
+ """
+ img = labels["img"]
+ if self.hgain or self.sgain or self.vgain:
+ r = np.random.uniform(-1, 1, 3) * [self.hgain, self.sgain, self.vgain] + 1 # random gains
+ hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
+ dtype = img.dtype # uint8
+
+ x = np.arange(0, 256, dtype=r.dtype)
+ lut_hue = ((x * r[0]) % 180).astype(dtype)
+ lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
+ lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
+
+ im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
+ cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed
+ return labels
+
+
+class RandomFlip:
+ """
+ Applies a random horizontal or vertical flip to an image with a given probability.
+
+ This class performs random image flipping and updates corresponding instance annotations such as
+ bounding boxes and keypoints.
+
+ Attributes:
+ p (float): Probability of applying the flip. Must be between 0 and 1.
+ direction (str): Direction of flip, either 'horizontal' or 'vertical'.
+ flip_idx (array-like): Index mapping for flipping keypoints, if applicable.
+
+ Methods:
+ __call__: Applies the random flip transformation to an image and its annotations.
+
+ Examples:
+ >>> transform = RandomFlip(p=0.5, direction="horizontal")
+ >>> result = transform({"img": image, "instances": instances})
+ >>> flipped_image = result["img"]
+ >>> flipped_instances = result["instances"]
+ """
+
+ def __init__(self, p=0.5, direction="horizontal", flip_idx=None) -> None:
+ """
+ Initializes the RandomFlip class with probability and direction.
+
+ This class applies a random horizontal or vertical flip to an image with a given probability.
+ It also updates any instances (bounding boxes, keypoints, etc.) accordingly.
+
+ Args:
+ p (float): The probability of applying the flip. Must be between 0 and 1.
+ direction (str): The direction to apply the flip. Must be 'horizontal' or 'vertical'.
+ flip_idx (List[int] | None): Index mapping for flipping keypoints, if any.
+
+ Raises:
+ AssertionError: If direction is not 'horizontal' or 'vertical', or if p is not between 0 and 1.
+
+ Examples:
+ >>> flip = RandomFlip(p=0.5, direction="horizontal")
+ >>> flip_with_idx = RandomFlip(p=0.7, direction="vertical", flip_idx=[1, 0, 3, 2, 5, 4])
+ """
+ assert direction in {"horizontal", "vertical"}, f"Support direction `horizontal` or `vertical`, got {direction}"
+ assert 0 <= p <= 1.0, f"The probability should be in range [0, 1], but got {p}."
+
+ self.p = p
+ self.direction = direction
+ self.flip_idx = flip_idx
+
+ def __call__(self, labels):
+ """
+ Applies random flip to an image and updates any instances like bounding boxes or keypoints accordingly.
+
+ This method randomly flips the input image either horizontally or vertically based on the initialized
+ probability and direction. It also updates the corresponding instances (bounding boxes, keypoints) to
+ match the flipped image.
+
+ Args:
+ labels (Dict): A dictionary containing the following keys:
+ 'img' (numpy.ndarray): The image to be flipped.
+ 'instances' (ultralytics.utils.instance.Instances): An object containing bounding boxes and
+ optionally keypoints.
+
+ Returns:
+ (Dict): The same dictionary with the flipped image and updated instances:
+ 'img' (numpy.ndarray): The flipped image.
+ 'instances' (ultralytics.utils.instance.Instances): Updated instances matching the flipped image.
+
+ Examples:
+ >>> labels = {"img": np.random.rand(640, 640, 3), "instances": Instances(...)}
+ >>> random_flip = RandomFlip(p=0.5, direction="horizontal")
+ >>> flipped_labels = random_flip(labels)
+ """
+ img = labels["img"]
+ instances = labels.pop("instances")
+ instances.convert_bbox(format="xywh")
+ h, w = img.shape[:2]
+ h = 1 if instances.normalized else h
+ w = 1 if instances.normalized else w
+
+ # Flip up-down
+ if self.direction == "vertical" and random.random() < self.p:
+ img = np.flipud(img)
+ instances.flipud(h)
+ if self.direction == "horizontal" and random.random() < self.p:
+ img = np.fliplr(img)
+ instances.fliplr(w)
+ # For keypoints
+ if self.flip_idx is not None and instances.keypoints is not None:
+ instances.keypoints = np.ascontiguousarray(instances.keypoints[:, self.flip_idx, :])
+ labels["img"] = np.ascontiguousarray(img)
+ labels["instances"] = instances
+ return labels
+
+
+class LetterBox:
+ """
+ Resize image and padding for detection, instance segmentation, pose.
+
+ This class resizes and pads images to a specified shape while preserving aspect ratio. It also updates
+ corresponding labels and bounding boxes.
+
+ Attributes:
+ new_shape (tuple): Target shape (height, width) for resizing.
+ auto (bool): Whether to use minimum rectangle.
+ scaleFill (bool): Whether to stretch the image to new_shape.
+ scaleup (bool): Whether to allow scaling up. If False, only scale down.
+ stride (int): Stride for rounding padding.
+ center (bool): Whether to center the image or align to top-left.
+
+ Methods:
+ __call__: Resize and pad image, update labels and bounding boxes.
+
+ Examples:
+ >>> transform = LetterBox(new_shape=(640, 640))
+ >>> result = transform(labels)
+ >>> resized_img = result["img"]
+ >>> updated_instances = result["instances"]
+ """
+
+ def __init__(self, new_shape=(640, 640), auto=False, scaleFill=False, scaleup=True, center=True, stride=32):
+ """
+ Initialize LetterBox object for resizing and padding images.
+
+ This class is designed to resize and pad images for object detection, instance segmentation, and pose estimation
+ tasks. It supports various resizing modes including auto-sizing, scale-fill, and letterboxing.
+
+ Args:
+ new_shape (Tuple[int, int]): Target size (height, width) for the resized image.
+ auto (bool): If True, use minimum rectangle to resize. If False, use new_shape directly.
+ scaleFill (bool): If True, stretch the image to new_shape without padding.
+ scaleup (bool): If True, allow scaling up. If False, only scale down.
+ center (bool): If True, center the placed image. If False, place image in top-left corner.
+ stride (int): Stride of the model (e.g., 32 for YOLOv5).
+
+ Attributes:
+ new_shape (Tuple[int, int]): Target size for the resized image.
+ auto (bool): Flag for using minimum rectangle resizing.
+ scaleFill (bool): Flag for stretching image without padding.
+ scaleup (bool): Flag for allowing upscaling.
+ stride (int): Stride value for ensuring image size is divisible by stride.
+
+ Examples:
+ >>> letterbox = LetterBox(new_shape=(640, 640), auto=False, scaleFill=False, scaleup=True, stride=32)
+ >>> resized_img = letterbox(original_img)
+ """
+ self.new_shape = new_shape
+ self.auto = auto
+ self.scaleFill = scaleFill
+ self.scaleup = scaleup
+ self.stride = stride
+ self.center = center # Put the image in the middle or top-left
+
+ def __call__(self, labels=None, image=None):
+ """
+ Resizes and pads an image for object detection, instance segmentation, or pose estimation tasks.
+
+ This method applies letterboxing to the input image, which involves resizing the image while maintaining its
+ aspect ratio and adding padding to fit the new shape. It also updates any associated labels accordingly.
+
+ Args:
+ labels (Dict | None): A dictionary containing image data and associated labels, or empty dict if None.
+ image (np.ndarray | None): The input image as a numpy array. If None, the image is taken from 'labels'.
+
+ Returns:
+ (Dict | Tuple): If 'labels' is provided, returns an updated dictionary with the resized and padded image,
+ updated labels, and additional metadata. If 'labels' is empty, returns a tuple containing the resized
+ and padded image, and a tuple of (ratio, (left_pad, top_pad)).
+
+ Examples:
+ >>> letterbox = LetterBox(new_shape=(640, 640))
+ >>> result = letterbox(labels={"img": np.zeros((480, 640, 3)), "instances": Instances(...)})
+ >>> resized_img = result["img"]
+ >>> updated_instances = result["instances"]
+ """
+ if labels is None:
+ labels = {}
+ img = labels.get("img") if image is None else image
+ shape = img.shape[:2] # current shape [height, width]
+ new_shape = labels.pop("rect_shape", self.new_shape)
+ if isinstance(new_shape, int):
+ new_shape = (new_shape, new_shape)
+
+ # Scale ratio (new / old)
+ r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+ if not self.scaleup: # only scale down, do not scale up (for better val mAP)
+ r = min(r, 1.0)
+
+ # Compute padding
+ ratio = r, r # width, height ratios
+ new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+ dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
+ if self.auto: # minimum rectangle
+ dw, dh = np.mod(dw, self.stride), np.mod(dh, self.stride) # wh padding
+ elif self.scaleFill: # stretch
+ dw, dh = 0.0, 0.0
+ new_unpad = (new_shape[1], new_shape[0])
+ ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
+
+ if self.center:
+ dw /= 2 # divide padding into 2 sides
+ dh /= 2
+
+ if shape[::-1] != new_unpad: # resize
+ img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
+ top, bottom = int(round(dh - 0.1)) if self.center else 0, int(round(dh + 0.1))
+ left, right = int(round(dw - 0.1)) if self.center else 0, int(round(dw + 0.1))
+ img = cv2.copyMakeBorder(
+ img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114)
+ ) # add border
+ if labels.get("ratio_pad"):
+ labels["ratio_pad"] = (labels["ratio_pad"], (left, top)) # for evaluation
+
+ if len(labels):
+ labels = self._update_labels(labels, ratio, left, top)
+ labels["img"] = img
+ labels["resized_shape"] = new_shape
+ return labels
+ else:
+ return img
+
+ @staticmethod
+ def _update_labels(labels, ratio, padw, padh):
+ """
+ Updates labels after applying letterboxing to an image.
+
+ This method modifies the bounding box coordinates of instances in the labels
+ to account for resizing and padding applied during letterboxing.
+
+ Args:
+ labels (Dict): A dictionary containing image labels and instances.
+ ratio (Tuple[float, float]): Scaling ratios (width, height) applied to the image.
+ padw (float): Padding width added to the image.
+ padh (float): Padding height added to the image.
+
+ Returns:
+ (Dict): Updated labels dictionary with modified instance coordinates.
+
+ Examples:
+ >>> letterbox = LetterBox(new_shape=(640, 640))
+ >>> labels = {"instances": Instances(...)}
+ >>> ratio = (0.5, 0.5)
+ >>> padw, padh = 10, 20
+ >>> updated_labels = letterbox._update_labels(labels, ratio, padw, padh)
+ """
+ labels["instances"].convert_bbox(format="xyxy")
+ labels["instances"].denormalize(*labels["img"].shape[:2][::-1])
+ labels["instances"].scale(*ratio)
+ labels["instances"].add_padding(padw, padh)
+ return labels
+
+
+class CopyPaste(BaseMixTransform):
+ """
+ CopyPaste class for applying Copy-Paste augmentation to image datasets.
+
+ This class implements the Copy-Paste augmentation technique as described in the paper "Simple Copy-Paste is a Strong
+ Data Augmentation Method for Instance Segmentation" (https://arxiv.org/abs/2012.07177). It combines objects from
+ different images to create new training samples.
+
+ Attributes:
+ dataset (Any): The dataset to which Copy-Paste augmentation will be applied.
+ pre_transform (Callable | None): Optional transform to apply before Copy-Paste.
+ p (float): Probability of applying Copy-Paste augmentation.
+
+ Methods:
+ get_indexes: Returns a random index from the dataset.
+ _mix_transform: Applies Copy-Paste augmentation to the input labels.
+ __call__: Applies the Copy-Paste transformation to images and annotations.
+
+ Examples:
+ >>> from ultralytics.data.augment import CopyPaste
+ >>> dataset = YourDataset(...) # Your image dataset
+ >>> copypaste = CopyPaste(dataset, p=0.5)
+ >>> augmented_labels = copypaste(original_labels)
+ """
+
+ def __init__(self, dataset=None, pre_transform=None, p=0.5, mode="flip") -> None:
+ """Initializes CopyPaste object with dataset, pre_transform, and probability of applying MixUp."""
+ super().__init__(dataset=dataset, pre_transform=pre_transform, p=p)
+ assert mode in {"flip", "mixup"}, f"Expected `mode` to be `flip` or `mixup`, but got {mode}."
+ self.mode = mode
+
+ def get_indexes(self):
+ """Returns a list of random indexes from the dataset for CopyPaste augmentation."""
+ return random.randint(0, len(self.dataset) - 1)
+
+ def _mix_transform(self, labels):
+ """Applies Copy-Paste augmentation to combine objects from another image into the current image."""
+ labels2 = labels["mix_labels"][0]
+ return self._transform(labels, labels2)
+
+ def __call__(self, labels):
+ """Applies Copy-Paste augmentation to an image and its labels."""
+ if len(labels["instances"].segments) == 0 or self.p == 0:
+ return labels
+ if self.mode == "flip":
+ return self._transform(labels)
+
+ # Get index of one or three other images
+ indexes = self.get_indexes()
+ if isinstance(indexes, int):
+ indexes = [indexes]
+
+ # Get images information will be used for Mosaic or MixUp
+ mix_labels = [self.dataset.get_image_and_label(i) for i in indexes]
+
+ if self.pre_transform is not None:
+ for i, data in enumerate(mix_labels):
+ mix_labels[i] = self.pre_transform(data)
+ labels["mix_labels"] = mix_labels
+
+ # Update cls and texts
+ labels = self._update_label_text(labels)
+ # Mosaic or MixUp
+ labels = self._mix_transform(labels)
+ labels.pop("mix_labels", None)
+ return labels
+
+ def _transform(self, labels1, labels2={}):
+ """Applies Copy-Paste augmentation to combine objects from another image into the current image."""
+ im = labels1["img"]
+ cls = labels1["cls"]
+ h, w = im.shape[:2]
+ instances = labels1.pop("instances")
+ instances.convert_bbox(format="xyxy")
+ instances.denormalize(w, h)
+
+ im_new = np.zeros(im.shape, np.uint8)
+ instances2 = labels2.pop("instances", None)
+ if instances2 is None:
+ instances2 = deepcopy(instances)
+ instances2.fliplr(w)
+ ioa = bbox_ioa(instances2.bboxes, instances.bboxes) # intersection over area, (N, M)
+ indexes = np.nonzero((ioa < 0.30).all(1))[0] # (N, )
+ n = len(indexes)
+ sorted_idx = np.argsort(ioa.max(1)[indexes])
+ indexes = indexes[sorted_idx]
+ for j in indexes[: round(self.p * n)]:
+ cls = np.concatenate((cls, labels2.get("cls", cls)[[j]]), axis=0)
+ instances = Instances.concatenate((instances, instances2[[j]]), axis=0)
+ cv2.drawContours(im_new, instances2.segments[[j]].astype(np.int32), -1, (1, 1, 1), cv2.FILLED)
+
+ result = labels2.get("img", cv2.flip(im, 1)) # augment segments
+ i = im_new.astype(bool)
+ im[i] = result[i]
+
+ labels1["img"] = im
+ labels1["cls"] = cls
+ labels1["instances"] = instances
+ return labels1
+
+
+class Albumentations:
+ """
+ Albumentations transformations for image augmentation.
+
+ This class applies various image transformations using the Albumentations library. It includes operations such as
+ Blur, Median Blur, conversion to grayscale, Contrast Limited Adaptive Histogram Equalization (CLAHE), random changes
+ in brightness and contrast, RandomGamma, and image quality reduction through compression.
+
+ Attributes:
+ p (float): Probability of applying the transformations.
+ transform (albumentations.Compose): Composed Albumentations transforms.
+ contains_spatial (bool): Indicates if the transforms include spatial operations.
+
+ Methods:
+ __call__: Applies the Albumentations transformations to the input labels.
+
+ Examples:
+ >>> transform = Albumentations(p=0.5)
+ >>> augmented_labels = transform(labels)
+
+ Notes:
+ - The Albumentations package must be installed to use this class.
+ - If the package is not installed or an error occurs during initialization, the transform will be set to None.
+ - Spatial transforms are handled differently and require special processing for bounding boxes.
+ """
+
+ def __init__(self, p=1.0):
+ """
+ Initialize the Albumentations transform object for YOLO bbox formatted parameters.
+
+ This class applies various image augmentations using the Albumentations library, including Blur, Median Blur,
+ conversion to grayscale, Contrast Limited Adaptive Histogram Equalization, random changes of brightness and
+ contrast, RandomGamma, and image quality reduction through compression.
+
+ Args:
+ p (float): Probability of applying the augmentations. Must be between 0 and 1.
+
+ Attributes:
+ p (float): Probability of applying the augmentations.
+ transform (albumentations.Compose): Composed Albumentations transforms.
+ contains_spatial (bool): Indicates if the transforms include spatial transformations.
+
+ Raises:
+ ImportError: If the Albumentations package is not installed.
+ Exception: For any other errors during initialization.
+
+ Examples:
+ >>> transform = Albumentations(p=0.5)
+ >>> augmented = transform(image=image, bboxes=bboxes, class_labels=classes)
+ >>> augmented_image = augmented["image"]
+ >>> augmented_bboxes = augmented["bboxes"]
+
+ Notes:
+ - Requires Albumentations version 1.0.3 or higher.
+ - Spatial transforms are handled differently to ensure bbox compatibility.
+ - Some transforms are applied with very low probability (0.01) by default.
+ """
+ self.p = p
+ self.transform = None
+ prefix = colorstr("albumentations: ")
+
+ try:
+ import albumentations as A
+
+ check_version(A.__version__, "1.0.3", hard=True) # version requirement
+
+ # List of possible spatial transforms
+ spatial_transforms = {
+ "Affine",
+ "BBoxSafeRandomCrop",
+ "CenterCrop",
+ "CoarseDropout",
+ "Crop",
+ "CropAndPad",
+ "CropNonEmptyMaskIfExists",
+ "D4",
+ "ElasticTransform",
+ "Flip",
+ "GridDistortion",
+ "GridDropout",
+ "HorizontalFlip",
+ "Lambda",
+ "LongestMaxSize",
+ "MaskDropout",
+ "MixUp",
+ "Morphological",
+ "NoOp",
+ "OpticalDistortion",
+ "PadIfNeeded",
+ "Perspective",
+ "PiecewiseAffine",
+ "PixelDropout",
+ "RandomCrop",
+ "RandomCropFromBorders",
+ "RandomGridShuffle",
+ "RandomResizedCrop",
+ "RandomRotate90",
+ "RandomScale",
+ "RandomSizedBBoxSafeCrop",
+ "RandomSizedCrop",
+ "Resize",
+ "Rotate",
+ "SafeRotate",
+ "ShiftScaleRotate",
+ "SmallestMaxSize",
+ "Transpose",
+ "VerticalFlip",
+ "XYMasking",
+ } # from https://albumentations.ai/docs/getting_started/transforms_and_targets/#spatial-level-transforms
+
+ # Transforms
+ T = [
+ A.Blur(p=0.01),
+ A.MedianBlur(p=0.01),
+ A.ToGray(p=0.01),
+ A.CLAHE(p=0.01),
+ A.RandomBrightnessContrast(p=0.0),
+ A.RandomGamma(p=0.0),
+ A.ImageCompression(quality_lower=75, p=0.0),
+ ]
+
+ # Compose transforms
+ self.contains_spatial = any(transform.__class__.__name__ in spatial_transforms for transform in T)
+ self.transform = (
+ A.Compose(T, bbox_params=A.BboxParams(format="yolo", label_fields=["class_labels"]))
+ if self.contains_spatial
+ else A.Compose(T)
+ )
+ if hasattr(self.transform, "set_random_seed"):
+ # Required for deterministic transforms in albumentations>=1.4.21
+ self.transform.set_random_seed(torch.initial_seed())
+ LOGGER.info(prefix + ", ".join(f"{x}".replace("always_apply=False, ", "") for x in T if x.p))
+ except ImportError: # package not installed, skip
+ pass
+ except Exception as e:
+ LOGGER.info(f"{prefix}{e}")
+
+ def __call__(self, labels):
+ """
+ Applies Albumentations transformations to input labels.
+
+ This method applies a series of image augmentations using the Albumentations library. It can perform both
+ spatial and non-spatial transformations on the input image and its corresponding labels.
+
+ Args:
+ labels (Dict): A dictionary containing image data and annotations. Expected keys are:
+ - 'img': numpy.ndarray representing the image
+ - 'cls': numpy.ndarray of class labels
+ - 'instances': object containing bounding boxes and other instance information
+
+ Returns:
+ (Dict): The input dictionary with augmented image and updated annotations.
+
+ Examples:
+ >>> transform = Albumentations(p=0.5)
+ >>> labels = {
+ ... "img": np.random.rand(640, 640, 3),
+ ... "cls": np.array([0, 1]),
+ ... "instances": Instances(bboxes=np.array([[0, 0, 1, 1], [0.5, 0.5, 0.8, 0.8]])),
+ ... }
+ >>> augmented = transform(labels)
+ >>> assert augmented["img"].shape == (640, 640, 3)
+
+ Notes:
+ - The method applies transformations with probability self.p.
+ - Spatial transforms update bounding boxes, while non-spatial transforms only modify the image.
+ - Requires the Albumentations library to be installed.
+ """
+ if self.transform is None or random.random() > self.p:
+ return labels
+
+ if self.contains_spatial:
+ cls = labels["cls"]
+ if len(cls):
+ im = labels["img"]
+ labels["instances"].convert_bbox("xywh")
+ labels["instances"].normalize(*im.shape[:2][::-1])
+ bboxes = labels["instances"].bboxes
+ # TODO: add supports of segments and keypoints
+ new = self.transform(image=im, bboxes=bboxes, class_labels=cls) # transformed
+ if len(new["class_labels"]) > 0: # skip update if no bbox in new im
+ labels["img"] = new["image"]
+ labels["cls"] = np.array(new["class_labels"])
+ bboxes = np.array(new["bboxes"], dtype=np.float32)
+ labels["instances"].update(bboxes=bboxes)
+ else:
+ labels["img"] = self.transform(image=labels["img"])["image"] # transformed
+
+ return labels
+
+
+class Format:
+ """
+ A class for formatting image annotations for object detection, instance segmentation, and pose estimation tasks.
+
+ This class standardizes image and instance annotations to be used by the `collate_fn` in PyTorch DataLoader.
+
+ Attributes:
+ bbox_format (str): Format for bounding boxes. Options are 'xywh' or 'xyxy'.
+ normalize (bool): Whether to normalize bounding boxes.
+ return_mask (bool): Whether to return instance masks for segmentation.
+ return_keypoint (bool): Whether to return keypoints for pose estimation.
+ return_obb (bool): Whether to return oriented bounding boxes.
+ mask_ratio (int): Downsample ratio for masks.
+ mask_overlap (bool): Whether to overlap masks.
+ batch_idx (bool): Whether to keep batch indexes.
+ bgr (float): The probability to return BGR images.
+
+ Methods:
+ __call__: Formats labels dictionary with image, classes, bounding boxes, and optionally masks and keypoints.
+ _format_img: Converts image from Numpy array to PyTorch tensor.
+ _format_segments: Converts polygon points to bitmap masks.
+
+ Examples:
+ >>> formatter = Format(bbox_format="xywh", normalize=True, return_mask=True)
+ >>> formatted_labels = formatter(labels)
+ >>> img = formatted_labels["img"]
+ >>> bboxes = formatted_labels["bboxes"]
+ >>> masks = formatted_labels["masks"]
+ """
+
+ def __init__(
+ self,
+ bbox_format="xywh",
+ normalize=True,
+ return_mask=False,
+ return_keypoint=False,
+ return_obb=False,
+ mask_ratio=4,
+ mask_overlap=True,
+ batch_idx=True,
+ bgr=0.0,
+ ):
+ """
+ Initializes the Format class with given parameters for image and instance annotation formatting.
+
+ This class standardizes image and instance annotations for object detection, instance segmentation, and pose
+ estimation tasks, preparing them for use in PyTorch DataLoader's `collate_fn`.
+
+ Args:
+ bbox_format (str): Format for bounding boxes. Options are 'xywh', 'xyxy', etc.
+ normalize (bool): Whether to normalize bounding boxes to [0,1].
+ return_mask (bool): If True, returns instance masks for segmentation tasks.
+ return_keypoint (bool): If True, returns keypoints for pose estimation tasks.
+ return_obb (bool): If True, returns oriented bounding boxes.
+ mask_ratio (int): Downsample ratio for masks.
+ mask_overlap (bool): If True, allows mask overlap.
+ batch_idx (bool): If True, keeps batch indexes.
+ bgr (float): Probability of returning BGR images instead of RGB.
+
+ Attributes:
+ bbox_format (str): Format for bounding boxes.
+ normalize (bool): Whether bounding boxes are normalized.
+ return_mask (bool): Whether to return instance masks.
+ return_keypoint (bool): Whether to return keypoints.
+ return_obb (bool): Whether to return oriented bounding boxes.
+ mask_ratio (int): Downsample ratio for masks.
+ mask_overlap (bool): Whether masks can overlap.
+ batch_idx (bool): Whether to keep batch indexes.
+ bgr (float): The probability to return BGR images.
+
+ Examples:
+ >>> format = Format(bbox_format="xyxy", return_mask=True, return_keypoint=False)
+ >>> print(format.bbox_format)
+ xyxy
+ """
+ self.bbox_format = bbox_format
+ self.normalize = normalize
+ self.return_mask = return_mask # set False when training detection only
+ self.return_keypoint = return_keypoint
+ self.return_obb = return_obb
+ self.mask_ratio = mask_ratio
+ self.mask_overlap = mask_overlap
+ self.batch_idx = batch_idx # keep the batch indexes
+ self.bgr = bgr
+
+ def __call__(self, labels):
+ """
+ Formats image annotations for object detection, instance segmentation, and pose estimation tasks.
+
+ This method standardizes the image and instance annotations to be used by the `collate_fn` in PyTorch
+ DataLoader. It processes the input labels dictionary, converting annotations to the specified format and
+ applying normalization if required.
+
+ Args:
+ labels (Dict): A dictionary containing image and annotation data with the following keys:
+ - 'img': The input image as a numpy array.
+ - 'cls': Class labels for instances.
+ - 'instances': An Instances object containing bounding boxes, segments, and keypoints.
+
+ Returns:
+ (Dict): A dictionary with formatted data, including:
+ - 'img': Formatted image tensor.
+ - 'cls': Class label's tensor.
+ - 'bboxes': Bounding boxes tensor in the specified format.
+ - 'masks': Instance masks tensor (if return_mask is True).
+ - 'keypoints': Keypoints tensor (if return_keypoint is True).
+ - 'batch_idx': Batch index tensor (if batch_idx is True).
+
+ Examples:
+ >>> formatter = Format(bbox_format="xywh", normalize=True, return_mask=True)
+ >>> labels = {"img": np.random.rand(640, 640, 3), "cls": np.array([0, 1]), "instances": Instances(...)}
+ >>> formatted_labels = formatter(labels)
+ >>> print(formatted_labels.keys())
+ """
+ img = labels.pop("img")
+ h, w = img.shape[:2]
+ cls = labels.pop("cls")
+ instances = labels.pop("instances")
+ instances.convert_bbox(format=self.bbox_format)
+ instances.denormalize(w, h)
+ nl = len(instances)
+
+ if self.return_mask:
+ if nl:
+ masks, instances, cls = self._format_segments(instances, cls, w, h)
+ masks = torch.from_numpy(masks)
+ else:
+ masks = torch.zeros(
+ 1 if self.mask_overlap else nl, img.shape[0] // self.mask_ratio, img.shape[1] // self.mask_ratio
+ )
+ labels["masks"] = masks
+ labels["img"] = self._format_img(img)
+ labels["cls"] = torch.from_numpy(cls) if nl else torch.zeros(nl)
+ labels["bboxes"] = torch.from_numpy(instances.bboxes) if nl else torch.zeros((nl, 4))
+ if self.return_keypoint:
+ labels["keypoints"] = torch.from_numpy(instances.keypoints)
+ if self.normalize:
+ labels["keypoints"][..., 0] /= w
+ labels["keypoints"][..., 1] /= h
+ if self.return_obb:
+ labels["bboxes"] = (
+ xyxyxyxy2xywhr(torch.from_numpy(instances.segments)) if len(instances.segments) else torch.zeros((0, 5))
+ )
+ # NOTE: need to normalize obb in xywhr format for width-height consistency
+ if self.normalize:
+ labels["bboxes"][:, [0, 2]] /= w
+ labels["bboxes"][:, [1, 3]] /= h
+ # Then we can use collate_fn
+ if self.batch_idx:
+ labels["batch_idx"] = torch.zeros(nl)
+ return labels
+
+ def _format_img(self, img):
+ """
+ Formats an image for YOLO from a Numpy array to a PyTorch tensor.
+
+ This function performs the following operations:
+ 1. Ensures the image has 3 dimensions (adds a channel dimension if needed).
+ 2. Transposes the image from HWC to CHW format.
+ 3. Optionally flips the color channels from RGB to BGR.
+ 4. Converts the image to a contiguous array.
+ 5. Converts the Numpy array to a PyTorch tensor.
+
+ Args:
+ img (np.ndarray): Input image as a Numpy array with shape (H, W, C) or (H, W).
+
+ Returns:
+ (torch.Tensor): Formatted image as a PyTorch tensor with shape (C, H, W).
+
+ Examples:
+ >>> import numpy as np
+ >>> img = np.random.rand(100, 100, 3)
+ >>> formatted_img = self._format_img(img)
+ >>> print(formatted_img.shape)
+ torch.Size([3, 100, 100])
+ """
+ if len(img.shape) < 3:
+ img = np.expand_dims(img, -1)
+ img = img.transpose(2, 0, 1)
+ img = np.ascontiguousarray(img[::-1] if random.uniform(0, 1) > self.bgr else img)
+ img = torch.from_numpy(img)
+ return img
+
+ def _format_segments(self, instances, cls, w, h):
+ """
+ Converts polygon segments to bitmap masks.
+
+ Args:
+ instances (Instances): Object containing segment information.
+ cls (numpy.ndarray): Class labels for each instance.
+ w (int): Width of the image.
+ h (int): Height of the image.
+
+ Returns:
+ masks (numpy.ndarray): Bitmap masks with shape (N, H, W) or (1, H, W) if mask_overlap is True.
+ instances (Instances): Updated instances object with sorted segments if mask_overlap is True.
+ cls (numpy.ndarray): Updated class labels, sorted if mask_overlap is True.
+
+ Notes:
+ - If self.mask_overlap is True, masks are overlapped and sorted by area.
+ - If self.mask_overlap is False, each mask is represented separately.
+ - Masks are downsampled according to self.mask_ratio.
+ """
+ segments = instances.segments
+ if self.mask_overlap:
+ masks, sorted_idx = polygons2masks_overlap((h, w), segments, downsample_ratio=self.mask_ratio)
+ masks = masks[None] # (640, 640) -> (1, 640, 640)
+ instances = instances[sorted_idx]
+ cls = cls[sorted_idx]
+ else:
+ masks = polygons2masks((h, w), segments, color=1, downsample_ratio=self.mask_ratio)
+
+ return masks, instances, cls
+
+
+class RandomLoadText:
+ """
+ Randomly samples positive and negative texts and updates class indices accordingly.
+
+ This class is responsible for sampling texts from a given set of class texts, including both positive
+ (present in the image) and negative (not present in the image) samples. It updates the class indices
+ to reflect the sampled texts and can optionally pad the text list to a fixed length.
+
+ Attributes:
+ prompt_format (str): Format string for text prompts.
+ neg_samples (Tuple[int, int]): Range for randomly sampling negative texts.
+ max_samples (int): Maximum number of different text samples in one image.
+ padding (bool): Whether to pad texts to max_samples.
+ padding_value (str): The text used for padding when padding is True.
+
+ Methods:
+ __call__: Processes the input labels and returns updated classes and texts.
+
+ Examples:
+ >>> loader = RandomLoadText(prompt_format="Object: {}", neg_samples=(5, 10), max_samples=20)
+ >>> labels = {"cls": [0, 1, 2], "texts": [["cat"], ["dog"], ["bird"]], "instances": [...]}
+ >>> updated_labels = loader(labels)
+ >>> print(updated_labels["texts"])
+ ['Object: cat', 'Object: dog', 'Object: bird', 'Object: elephant', 'Object: car']
+ """
+
+ def __init__(
+ self,
+ prompt_format: str = "{}",
+ neg_samples: Tuple[int, int] = (80, 80),
+ max_samples: int = 80,
+ padding: bool = False,
+ padding_value: str = "",
+ ) -> None:
+ """
+ Initializes the RandomLoadText class for randomly sampling positive and negative texts.
+
+ This class is designed to randomly sample positive texts and negative texts, and update the class
+ indices accordingly to the number of samples. It can be used for text-based object detection tasks.
+
+ Args:
+ prompt_format (str): Format string for the prompt. Default is '{}'. The format string should
+ contain a single pair of curly braces {} where the text will be inserted.
+ neg_samples (Tuple[int, int]): A range to randomly sample negative texts. The first integer
+ specifies the minimum number of negative samples, and the second integer specifies the
+ maximum. Default is (80, 80).
+ max_samples (int): The maximum number of different text samples in one image. Default is 80.
+ padding (bool): Whether to pad texts to max_samples. If True, the number of texts will always
+ be equal to max_samples. Default is False.
+ padding_value (str): The padding text to use when padding is True. Default is an empty string.
+
+ Attributes:
+ prompt_format (str): The format string for the prompt.
+ neg_samples (Tuple[int, int]): The range for sampling negative texts.
+ max_samples (int): The maximum number of text samples.
+ padding (bool): Whether padding is enabled.
+ padding_value (str): The value used for padding.
+
+ Examples:
+ >>> random_load_text = RandomLoadText(prompt_format="Object: {}", neg_samples=(50, 100), max_samples=120)
+ >>> random_load_text.prompt_format
+ 'Object: {}'
+ >>> random_load_text.neg_samples
+ (50, 100)
+ >>> random_load_text.max_samples
+ 120
+ """
+ self.prompt_format = prompt_format
+ self.neg_samples = neg_samples
+ self.max_samples = max_samples
+ self.padding = padding
+ self.padding_value = padding_value
+
+ def __call__(self, labels: dict) -> dict:
+ """
+ Randomly samples positive and negative texts and updates class indices accordingly.
+
+ This method samples positive texts based on the existing class labels in the image, and randomly
+ selects negative texts from the remaining classes. It then updates the class indices to match the
+ new sampled text order.
+
+ Args:
+ labels (Dict): A dictionary containing image labels and metadata. Must include 'texts' and 'cls' keys.
+
+ Returns:
+ (Dict): Updated labels dictionary with new 'cls' and 'texts' entries.
+
+ Examples:
+ >>> loader = RandomLoadText(prompt_format="A photo of {}", neg_samples=(5, 10), max_samples=20)
+ >>> labels = {"cls": np.array([[0], [1], [2]]), "texts": [["dog"], ["cat"], ["bird"]]}
+ >>> updated_labels = loader(labels)
+ """
+ assert "texts" in labels, "No texts found in labels."
+ class_texts = labels["texts"]
+ num_classes = len(class_texts)
+ cls = np.asarray(labels.pop("cls"), dtype=int)
+ pos_labels = np.unique(cls).tolist()
+
+ if len(pos_labels) > self.max_samples:
+ pos_labels = random.sample(pos_labels, k=self.max_samples)
+
+ neg_samples = min(min(num_classes, self.max_samples) - len(pos_labels), random.randint(*self.neg_samples))
+ neg_labels = [i for i in range(num_classes) if i not in pos_labels]
+ neg_labels = random.sample(neg_labels, k=neg_samples)
+
+ sampled_labels = pos_labels + neg_labels
+ random.shuffle(sampled_labels)
+
+ label2ids = {label: i for i, label in enumerate(sampled_labels)}
+ valid_idx = np.zeros(len(labels["instances"]), dtype=bool)
+ new_cls = []
+ for i, label in enumerate(cls.squeeze(-1).tolist()):
+ if label not in label2ids:
+ continue
+ valid_idx[i] = True
+ new_cls.append([label2ids[label]])
+ labels["instances"] = labels["instances"][valid_idx]
+ labels["cls"] = np.array(new_cls)
+
+ # Randomly select one prompt when there's more than one prompts
+ texts = []
+ for label in sampled_labels:
+ prompts = class_texts[label]
+ assert len(prompts) > 0
+ prompt = self.prompt_format.format(prompts[random.randrange(len(prompts))])
+ texts.append(prompt)
+
+ if self.padding:
+ valid_labels = len(pos_labels) + len(neg_labels)
+ num_padding = self.max_samples - valid_labels
+ if num_padding > 0:
+ texts += [self.padding_value] * num_padding
+
+ labels["texts"] = texts
+ return labels
+
+
+def v8_transforms(dataset, imgsz, hyp, stretch=False):
+ """
+ Applies a series of image transformations for training.
+
+ This function creates a composition of image augmentation techniques to prepare images for YOLO training.
+ It includes operations such as mosaic, copy-paste, random perspective, mixup, and various color adjustments.
+
+ Args:
+ dataset (Dataset): The dataset object containing image data and annotations.
+ imgsz (int): The target image size for resizing.
+ hyp (Namespace): A dictionary of hyperparameters controlling various aspects of the transformations.
+ stretch (bool): If True, applies stretching to the image. If False, uses LetterBox resizing.
+
+ Returns:
+ (Compose): A composition of image transformations to be applied to the dataset.
+
+ Examples:
+ >>> from ultralytics.data.dataset import YOLODataset
+ >>> from ultralytics.utils import IterableSimpleNamespace
+ >>> dataset = YOLODataset(img_path="path/to/images", imgsz=640)
+ >>> hyp = IterableSimpleNamespace(mosaic=1.0, copy_paste=0.5, degrees=10.0, translate=0.2, scale=0.9)
+ >>> transforms = v8_transforms(dataset, imgsz=640, hyp=hyp)
+ >>> augmented_data = transforms(dataset[0])
+ """
+ mosaic = Mosaic(dataset, imgsz=imgsz, p=hyp.mosaic)
+ affine = RandomPerspective(
+ degrees=hyp.degrees,
+ translate=hyp.translate,
+ scale=hyp.scale,
+ shear=hyp.shear,
+ perspective=hyp.perspective,
+ pre_transform=None if stretch else LetterBox(new_shape=(imgsz, imgsz)),
+ )
+
+ pre_transform = Compose([mosaic, affine])
+ if hyp.copy_paste_mode == "flip":
+ pre_transform.insert(1, CopyPaste(p=hyp.copy_paste, mode=hyp.copy_paste_mode))
+ else:
+ pre_transform.append(
+ CopyPaste(
+ dataset,
+ pre_transform=Compose([Mosaic(dataset, imgsz=imgsz, p=hyp.mosaic), affine]),
+ p=hyp.copy_paste,
+ mode=hyp.copy_paste_mode,
+ )
+ )
+ flip_idx = dataset.data.get("flip_idx", []) # for keypoints augmentation
+ if dataset.use_keypoints:
+ kpt_shape = dataset.data.get("kpt_shape", None)
+ if len(flip_idx) == 0 and hyp.fliplr > 0.0:
+ hyp.fliplr = 0.0
+ LOGGER.warning("WARNING ⚠️ No 'flip_idx' array defined in data.yaml, setting augmentation 'fliplr=0.0'")
+ elif flip_idx and (len(flip_idx) != kpt_shape[0]):
+ raise ValueError(f"data.yaml flip_idx={flip_idx} length must be equal to kpt_shape[0]={kpt_shape[0]}")
+
+ return Compose(
+ [
+ pre_transform,
+ MixUp(dataset, pre_transform=pre_transform, p=hyp.mixup),
+ Albumentations(p=1.0),
+ RandomHSV(hgain=hyp.hsv_h, sgain=hyp.hsv_s, vgain=hyp.hsv_v),
+ RandomFlip(direction="vertical", p=hyp.flipud),
+ RandomFlip(direction="horizontal", p=hyp.fliplr, flip_idx=flip_idx),
+ ]
+ ) # transforms
+
+
+# Classification augmentations -----------------------------------------------------------------------------------------
+def classify_transforms(
+ size=224,
+ mean=DEFAULT_MEAN,
+ std=DEFAULT_STD,
+ interpolation="BILINEAR",
+ crop_fraction: float = DEFAULT_CROP_FRACTION,
+):
+ """
+ Creates a composition of image transforms for classification tasks.
+
+ This function generates a sequence of torchvision transforms suitable for preprocessing images
+ for classification models during evaluation or inference. The transforms include resizing,
+ center cropping, conversion to tensor, and normalization.
+
+ Args:
+ size (int | tuple): The target size for the transformed image. If an int, it defines the shortest edge. If a
+ tuple, it defines (height, width).
+ mean (tuple): Mean values for each RGB channel used in normalization.
+ std (tuple): Standard deviation values for each RGB channel used in normalization.
+ interpolation (str): Interpolation method of either 'NEAREST', 'BILINEAR' or 'BICUBIC'.
+ crop_fraction (float): Fraction of the image to be cropped.
+
+ Returns:
+ (torchvision.transforms.Compose): A composition of torchvision transforms.
+
+ Examples:
+ >>> transforms = classify_transforms(size=224)
+ >>> img = Image.open("path/to/image.jpg")
+ >>> transformed_img = transforms(img)
+ """
+ import torchvision.transforms as T # scope for faster 'import ultralytics'
+
+ if isinstance(size, (tuple, list)):
+ assert len(size) == 2, f"'size' tuples must be length 2, not length {len(size)}"
+ scale_size = tuple(math.floor(x / crop_fraction) for x in size)
+ else:
+ scale_size = math.floor(size / crop_fraction)
+ scale_size = (scale_size, scale_size)
+
+ # Aspect ratio is preserved, crops center within image, no borders are added, image is lost
+ if scale_size[0] == scale_size[1]:
+ # Simple case, use torchvision built-in Resize with the shortest edge mode (scalar size arg)
+ tfl = [T.Resize(scale_size[0], interpolation=getattr(T.InterpolationMode, interpolation))]
+ else:
+ # Resize the shortest edge to matching target dim for non-square target
+ tfl = [T.Resize(scale_size)]
+ tfl.extend(
+ [
+ T.CenterCrop(size),
+ T.ToTensor(),
+ T.Normalize(mean=torch.tensor(mean), std=torch.tensor(std)),
+ ]
+ )
+ return T.Compose(tfl)
+
+
+# Classification training augmentations --------------------------------------------------------------------------------
+def classify_augmentations(
+ size=224,
+ mean=DEFAULT_MEAN,
+ std=DEFAULT_STD,
+ scale=None,
+ ratio=None,
+ hflip=0.5,
+ vflip=0.0,
+ auto_augment=None,
+ hsv_h=0.015, # image HSV-Hue augmentation (fraction)
+ hsv_s=0.4, # image HSV-Saturation augmentation (fraction)
+ hsv_v=0.4, # image HSV-Value augmentation (fraction)
+ force_color_jitter=False,
+ erasing=0.0,
+ interpolation="BILINEAR",
+):
+ """
+ Creates a composition of image augmentation transforms for classification tasks.
+
+ This function generates a set of image transformations suitable for training classification models. It includes
+ options for resizing, flipping, color jittering, auto augmentation, and random erasing.
+
+ Args:
+ size (int): Target size for the image after transformations.
+ mean (tuple): Mean values for normalization, one per channel.
+ std (tuple): Standard deviation values for normalization, one per channel.
+ scale (tuple | None): Range of size of the origin size cropped.
+ ratio (tuple | None): Range of aspect ratio of the origin aspect ratio cropped.
+ hflip (float): Probability of horizontal flip.
+ vflip (float): Probability of vertical flip.
+ auto_augment (str | None): Auto augmentation policy. Can be 'randaugment', 'augmix', 'autoaugment' or None.
+ hsv_h (float): Image HSV-Hue augmentation factor.
+ hsv_s (float): Image HSV-Saturation augmentation factor.
+ hsv_v (float): Image HSV-Value augmentation factor.
+ force_color_jitter (bool): Whether to apply color jitter even if auto augment is enabled.
+ erasing (float): Probability of random erasing.
+ interpolation (str): Interpolation method of either 'NEAREST', 'BILINEAR' or 'BICUBIC'.
+
+ Returns:
+ (torchvision.transforms.Compose): A composition of image augmentation transforms.
+
+ Examples:
+ >>> transforms = classify_augmentations(size=224, auto_augment="randaugment")
+ >>> augmented_image = transforms(original_image)
+ """
+ # Transforms to apply if Albumentations not installed
+ import torchvision.transforms as T # scope for faster 'import ultralytics'
+
+ if not isinstance(size, int):
+ raise TypeError(f"classify_transforms() size {size} must be integer, not (list, tuple)")
+ scale = tuple(scale or (0.08, 1.0)) # default imagenet scale range
+ ratio = tuple(ratio or (3.0 / 4.0, 4.0 / 3.0)) # default imagenet ratio range
+ interpolation = getattr(T.InterpolationMode, interpolation)
+ primary_tfl = [T.RandomResizedCrop(size, scale=scale, ratio=ratio, interpolation=interpolation)]
+ if hflip > 0.0:
+ primary_tfl.append(T.RandomHorizontalFlip(p=hflip))
+ if vflip > 0.0:
+ primary_tfl.append(T.RandomVerticalFlip(p=vflip))
+
+ secondary_tfl = []
+ disable_color_jitter = False
+ if auto_augment:
+ assert isinstance(auto_augment, str), f"Provided argument should be string, but got type {type(auto_augment)}"
+ # color jitter is typically disabled if AA/RA on,
+ # this allows override without breaking old hparm cfgs
+ disable_color_jitter = not force_color_jitter
+
+ if auto_augment == "randaugment":
+ if TORCHVISION_0_11:
+ secondary_tfl.append(T.RandAugment(interpolation=interpolation))
+ else:
+ LOGGER.warning('"auto_augment=randaugment" requires torchvision >= 0.11.0. Disabling it.')
+
+ elif auto_augment == "augmix":
+ if TORCHVISION_0_13:
+ secondary_tfl.append(T.AugMix(interpolation=interpolation))
+ else:
+ LOGGER.warning('"auto_augment=augmix" requires torchvision >= 0.13.0. Disabling it.')
+
+ elif auto_augment == "autoaugment":
+ if TORCHVISION_0_10:
+ secondary_tfl.append(T.AutoAugment(interpolation=interpolation))
+ else:
+ LOGGER.warning('"auto_augment=autoaugment" requires torchvision >= 0.10.0. Disabling it.')
+
+ else:
+ raise ValueError(
+ f'Invalid auto_augment policy: {auto_augment}. Should be one of "randaugment", '
+ f'"augmix", "autoaugment" or None'
+ )
+
+ if not disable_color_jitter:
+ secondary_tfl.append(T.ColorJitter(brightness=hsv_v, contrast=hsv_v, saturation=hsv_s, hue=hsv_h))
+
+ final_tfl = [
+ T.ToTensor(),
+ T.Normalize(mean=torch.tensor(mean), std=torch.tensor(std)),
+ T.RandomErasing(p=erasing, inplace=True),
+ ]
+
+ return T.Compose(primary_tfl + secondary_tfl + final_tfl)
+
+
+# NOTE: keep this class for backward compatibility
+class ClassifyLetterBox:
+ """
+ A class for resizing and padding images for classification tasks.
+
+ This class is designed to be part of a transformation pipeline, e.g., T.Compose([LetterBox(size), ToTensor()]).
+ It resizes and pads images to a specified size while maintaining the original aspect ratio.
+
+ Attributes:
+ h (int): Target height of the image.
+ w (int): Target width of the image.
+ auto (bool): If True, automatically calculates the short side using stride.
+ stride (int): The stride value, used when 'auto' is True.
+
+ Methods:
+ __call__: Applies the letterbox transformation to an input image.
+
+ Examples:
+ >>> transform = ClassifyLetterBox(size=(640, 640), auto=False, stride=32)
+ >>> img = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
+ >>> result = transform(img)
+ >>> print(result.shape)
+ (640, 640, 3)
+ """
+
+ def __init__(self, size=(640, 640), auto=False, stride=32):
+ """
+ Initializes the ClassifyLetterBox object for image preprocessing.
+
+ This class is designed to be part of a transformation pipeline for image classification tasks. It resizes and
+ pads images to a specified size while maintaining the original aspect ratio.
+
+ Args:
+ size (int | Tuple[int, int]): Target size for the letterboxed image. If an int, a square image of
+ (size, size) is created. If a tuple, it should be (height, width).
+ auto (bool): If True, automatically calculates the short side based on stride. Default is False.
+ stride (int): The stride value, used when 'auto' is True. Default is 32.
+
+ Attributes:
+ h (int): Target height of the letterboxed image.
+ w (int): Target width of the letterboxed image.
+ auto (bool): Flag indicating whether to automatically calculate short side.
+ stride (int): Stride value for automatic short side calculation.
+
+ Examples:
+ >>> transform = ClassifyLetterBox(size=224)
+ >>> img = np.random.randint(0, 255, (480, 640, 3), dtype=np.uint8)
+ >>> result = transform(img)
+ >>> print(result.shape)
+ (224, 224, 3)
+ """
+ super().__init__()
+ self.h, self.w = (size, size) if isinstance(size, int) else size
+ self.auto = auto # pass max size integer, automatically solve for short side using stride
+ self.stride = stride # used with auto
+
+ def __call__(self, im):
+ """
+ Resizes and pads an image using the letterbox method.
+
+ This method resizes the input image to fit within the specified dimensions while maintaining its aspect ratio,
+ then pads the resized image to match the target size.
+
+ Args:
+ im (numpy.ndarray): Input image as a numpy array with shape (H, W, C).
+
+ Returns:
+ (numpy.ndarray): Resized and padded image as a numpy array with shape (hs, ws, 3), where hs and ws are
+ the target height and width respectively.
+
+ Examples:
+ >>> letterbox = ClassifyLetterBox(size=(640, 640))
+ >>> image = np.random.randint(0, 255, (720, 1280, 3), dtype=np.uint8)
+ >>> resized_image = letterbox(image)
+ >>> print(resized_image.shape)
+ (640, 640, 3)
+ """
+ imh, imw = im.shape[:2]
+ r = min(self.h / imh, self.w / imw) # ratio of new/old dimensions
+ h, w = round(imh * r), round(imw * r) # resized image dimensions
+
+ # Calculate padding dimensions
+ hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else (self.h, self.w)
+ top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1)
+
+ # Create padded image
+ im_out = np.full((hs, ws, 3), 114, dtype=im.dtype)
+ im_out[top : top + h, left : left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)
+ return im_out
+
+
+# NOTE: keep this class for backward compatibility
+class CenterCrop:
+ """
+ Applies center cropping to images for classification tasks.
+
+ This class performs center cropping on input images, resizing them to a specified size while maintaining the aspect
+ ratio. It is designed to be part of a transformation pipeline, e.g., T.Compose([CenterCrop(size), ToTensor()]).
+
+ Attributes:
+ h (int): Target height of the cropped image.
+ w (int): Target width of the cropped image.
+
+ Methods:
+ __call__: Applies the center crop transformation to an input image.
+
+ Examples:
+ >>> transform = CenterCrop(640)
+ >>> image = np.random.randint(0, 255, (1080, 1920, 3), dtype=np.uint8)
+ >>> cropped_image = transform(image)
+ >>> print(cropped_image.shape)
+ (640, 640, 3)
+ """
+
+ def __init__(self, size=640):
+ """
+ Initializes the CenterCrop object for image preprocessing.
+
+ This class is designed to be part of a transformation pipeline, e.g., T.Compose([CenterCrop(size), ToTensor()]).
+ It performs a center crop on input images to a specified size.
+
+ Args:
+ size (int | Tuple[int, int]): The desired output size of the crop. If size is an int, a square crop
+ (size, size) is made. If size is a sequence like (h, w), it is used as the output size.
+
+ Returns:
+ (None): This method initializes the object and does not return anything.
+
+ Examples:
+ >>> transform = CenterCrop(224)
+ >>> img = np.random.rand(300, 300, 3)
+ >>> cropped_img = transform(img)
+ >>> print(cropped_img.shape)
+ (224, 224, 3)
+ """
+ super().__init__()
+ self.h, self.w = (size, size) if isinstance(size, int) else size
+
+ def __call__(self, im):
+ """
+ Applies center cropping to an input image.
+
+ This method resizes and crops the center of the image using a letterbox method. It maintains the aspect
+ ratio of the original image while fitting it into the specified dimensions.
+
+ Args:
+ im (numpy.ndarray | PIL.Image.Image): The input image as a numpy array of shape (H, W, C) or a
+ PIL Image object.
+
+ Returns:
+ (numpy.ndarray): The center-cropped and resized image as a numpy array of shape (self.h, self.w, C).
+
+ Examples:
+ >>> transform = CenterCrop(size=224)
+ >>> image = np.random.randint(0, 255, (640, 480, 3), dtype=np.uint8)
+ >>> cropped_image = transform(image)
+ >>> assert cropped_image.shape == (224, 224, 3)
+ """
+ if isinstance(im, Image.Image): # convert from PIL to numpy array if required
+ im = np.asarray(im)
+ imh, imw = im.shape[:2]
+ m = min(imh, imw) # min dimension
+ top, left = (imh - m) // 2, (imw - m) // 2
+ return cv2.resize(im[top : top + m, left : left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR)
+
+
+# NOTE: keep this class for backward compatibility
+class ToTensor:
+ """
+ Converts an image from a numpy array to a PyTorch tensor.
+
+ This class is designed to be part of a transformation pipeline, e.g., T.Compose([LetterBox(size), ToTensor()]).
+
+ Attributes:
+ half (bool): If True, converts the image to half precision (float16).
+
+ Methods:
+ __call__: Applies the tensor conversion to an input image.
+
+ Examples:
+ >>> transform = ToTensor(half=True)
+ >>> img = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
+ >>> tensor_img = transform(img)
+ >>> print(tensor_img.shape, tensor_img.dtype)
+ torch.Size([3, 640, 640]) torch.float16
+
+ Notes:
+ The input image is expected to be in BGR format with shape (H, W, C).
+ The output tensor will be in RGB format with shape (C, H, W), normalized to [0, 1].
+ """
+
+ def __init__(self, half=False):
+ """
+ Initializes the ToTensor object for converting images to PyTorch tensors.
+
+ This class is designed to be used as part of a transformation pipeline for image preprocessing in the
+ Ultralytics YOLO framework. It converts numpy arrays or PIL Images to PyTorch tensors, with an option
+ for half-precision (float16) conversion.
+
+ Args:
+ half (bool): If True, converts the tensor to half precision (float16). Default is False.
+
+ Examples:
+ >>> transform = ToTensor(half=True)
+ >>> img = np.random.rand(640, 640, 3)
+ >>> tensor_img = transform(img)
+ >>> print(tensor_img.dtype)
+ torch.float16
+ """
+ super().__init__()
+ self.half = half
+
+ def __call__(self, im):
+ """
+ Transforms an image from a numpy array to a PyTorch tensor.
+
+ This method converts the input image from a numpy array to a PyTorch tensor, applying optional
+ half-precision conversion and normalization. The image is transposed from HWC to CHW format and
+ the color channels are reversed from BGR to RGB.
+
+ Args:
+ im (numpy.ndarray): Input image as a numpy array with shape (H, W, C) in BGR order.
+
+ Returns:
+ (torch.Tensor): The transformed image as a PyTorch tensor in float32 or float16, normalized
+ to [0, 1] with shape (C, H, W) in RGB order.
+
+ Examples:
+ >>> transform = ToTensor(half=True)
+ >>> img = np.random.randint(0, 255, (640, 640, 3), dtype=np.uint8)
+ >>> tensor_img = transform(img)
+ >>> print(tensor_img.shape, tensor_img.dtype)
+ torch.Size([3, 640, 640]) torch.float16
+ """
+ im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1]) # HWC to CHW -> BGR to RGB -> contiguous
+ im = torch.from_numpy(im) # to torch
+ im = im.half() if self.half else im.float() # uint8 to fp16/32
+ im /= 255.0 # 0-255 to 0.0-1.0
+ return im
diff --git a/ultralytics/data/base.py b/ultralytics/data/base.py
new file mode 100644
index 0000000000000000000000000000000000000000..446b4ecf88b4e1193882ccc9681bc18c54559717
--- /dev/null
+++ b/ultralytics/data/base.py
@@ -0,0 +1,346 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import glob
+import math
+import os
+import random
+from copy import deepcopy
+from multiprocessing.pool import ThreadPool
+from pathlib import Path
+from typing import Optional
+
+import cv2
+import numpy as np
+import psutil
+from torch.utils.data import Dataset
+
+from ultralytics.data.utils import FORMATS_HELP_MSG, HELP_URL, IMG_FORMATS
+from ultralytics.utils import DEFAULT_CFG, LOCAL_RANK, LOGGER, NUM_THREADS, TQDM
+
+
+class BaseDataset(Dataset):
+ """
+ Base dataset class for loading and processing image data.
+
+ Args:
+ img_path (str): Path to the folder containing images.
+ imgsz (int, optional): Image size. Defaults to 640.
+ cache (bool, optional): Cache images to RAM or disk during training. Defaults to False.
+ augment (bool, optional): If True, data augmentation is applied. Defaults to True.
+ hyp (dict, optional): Hyperparameters to apply data augmentation. Defaults to None.
+ prefix (str, optional): Prefix to print in log messages. Defaults to ''.
+ rect (bool, optional): If True, rectangular training is used. Defaults to False.
+ batch_size (int, optional): Size of batches. Defaults to None.
+ stride (int, optional): Stride. Defaults to 32.
+ pad (float, optional): Padding. Defaults to 0.0.
+ single_cls (bool, optional): If True, single class training is used. Defaults to False.
+ classes (list): List of included classes. Default is None.
+ fraction (float): Fraction of dataset to utilize. Default is 1.0 (use all data).
+
+ Attributes:
+ im_files (list): List of image file paths.
+ labels (list): List of label data dictionaries.
+ ni (int): Number of images in the dataset.
+ ims (list): List of loaded images.
+ npy_files (list): List of numpy file paths.
+ transforms (callable): Image transformation function.
+ """
+
+ def __init__(
+ self,
+ img_path,
+ imgsz=640,
+ cache=False,
+ augment=True,
+ hyp=DEFAULT_CFG,
+ prefix="",
+ rect=False,
+ batch_size=16,
+ stride=32,
+ pad=0.5,
+ single_cls=False,
+ classes=None,
+ fraction=1.0,
+ ):
+ """Initialize BaseDataset with given configuration and options."""
+ super().__init__()
+ self.img_path = img_path
+ self.imgsz = imgsz
+ self.augment = augment
+ self.single_cls = single_cls
+ self.prefix = prefix
+ self.fraction = fraction
+ self.im_files = self.get_img_files(self.img_path)
+ self.labels = self.get_labels()
+ self.update_labels(include_class=classes) # single_cls and include_class
+ self.ni = len(self.labels) # number of images
+ self.rect = rect
+ self.batch_size = batch_size
+ self.stride = stride
+ self.pad = pad
+ if self.rect:
+ assert self.batch_size is not None
+ self.set_rectangle()
+
+ # Buffer thread for mosaic images
+ self.buffer = [] # buffer size = batch size
+ self.max_buffer_length = min((self.ni, self.batch_size * 8, 1000)) if self.augment else 0
+
+ # Cache images (options are cache = True, False, None, "ram", "disk")
+ self.ims, self.im_hw0, self.im_hw = [None] * self.ni, [None] * self.ni, [None] * self.ni
+ self.npy_files = [Path(f).with_suffix(".npy") for f in self.im_files]
+ self.cache = cache.lower() if isinstance(cache, str) else "ram" if cache is True else None
+ if self.cache == "ram" and self.check_cache_ram():
+ if hyp.deterministic:
+ LOGGER.warning(
+ "WARNING ⚠️ cache='ram' may produce non-deterministic training results. "
+ "Consider cache='disk' as a deterministic alternative if your disk space allows."
+ )
+ self.cache_images()
+ elif self.cache == "disk" and self.check_cache_disk():
+ self.cache_images()
+
+ # Transforms
+ self.transforms = self.build_transforms(hyp=hyp)
+
+ def get_img_files(self, img_path):
+ """Read image files."""
+ try:
+ f = [] # image files
+ for p in img_path if isinstance(img_path, list) else [img_path]:
+ p = Path(p) # os-agnostic
+ if p.is_dir(): # dir
+ f += glob.glob(str(p / "**" / "*.*"), recursive=True)
+ # F = list(p.rglob('*.*')) # pathlib
+ elif p.is_file(): # file
+ with open(p) as t:
+ t = t.read().strip().splitlines()
+ parent = str(p.parent) + os.sep
+ f += [x.replace("./", parent) if x.startswith("./") else x for x in t] # local to global path
+ # F += [p.parent / x.lstrip(os.sep) for x in t] # local to global path (pathlib)
+ else:
+ raise FileNotFoundError(f"{self.prefix}{p} does not exist")
+ im_files = sorted(x.replace("/", os.sep) for x in f if x.split(".")[-1].lower() in IMG_FORMATS)
+ # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS]) # pathlib
+ assert im_files, f"{self.prefix}No images found in {img_path}. {FORMATS_HELP_MSG}"
+ except Exception as e:
+ raise FileNotFoundError(f"{self.prefix}Error loading data from {img_path}\n{HELP_URL}") from e
+ if self.fraction < 1:
+ im_files = im_files[: round(len(im_files) * self.fraction)] # retain a fraction of the dataset
+ return im_files
+
+ def update_labels(self, include_class: Optional[list]):
+ """Update labels to include only these classes (optional)."""
+ include_class_array = np.array(include_class).reshape(1, -1)
+ for i in range(len(self.labels)):
+ if include_class is not None:
+ cls = self.labels[i]["cls"]
+ bboxes = self.labels[i]["bboxes"]
+ segments = self.labels[i]["segments"]
+ keypoints = self.labels[i]["keypoints"]
+ j = (cls == include_class_array).any(1)
+ self.labels[i]["cls"] = cls[j]
+ self.labels[i]["bboxes"] = bboxes[j]
+ if segments:
+ self.labels[i]["segments"] = [segments[si] for si, idx in enumerate(j) if idx]
+ if keypoints is not None:
+ self.labels[i]["keypoints"] = keypoints[j]
+ if self.single_cls:
+ self.labels[i]["cls"][:, 0] = 0
+
+ def load_image(self, i, rect_mode=True):
+ """Loads 1 image from dataset index 'i', returns (im, resized hw)."""
+ im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i]
+ if im is None: # not cached in RAM
+ if fn.exists(): # load npy
+ try:
+ im = np.load(fn)
+ except Exception as e:
+ LOGGER.warning(f"{self.prefix}WARNING ⚠️ Removing corrupt *.npy image file {fn} due to: {e}")
+ Path(fn).unlink(missing_ok=True)
+ im = cv2.imread(f) # BGR
+ else: # read image
+ im = cv2.imread(f) # BGR
+ if im is None:
+ raise FileNotFoundError(f"Image Not Found {f}")
+
+ h0, w0 = im.shape[:2] # orig hw
+ if rect_mode: # resize long side to imgsz while maintaining aspect ratio
+ r = self.imgsz / max(h0, w0) # ratio
+ if r != 1: # if sizes are not equal
+ w, h = (min(math.ceil(w0 * r), self.imgsz), min(math.ceil(h0 * r), self.imgsz))
+ im = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)
+ elif not (h0 == w0 == self.imgsz): # resize by stretching image to square imgsz
+ im = cv2.resize(im, (self.imgsz, self.imgsz), interpolation=cv2.INTER_LINEAR)
+
+ # Add to buffer if training with augmentations
+ if self.augment:
+ self.ims[i], self.im_hw0[i], self.im_hw[i] = im, (h0, w0), im.shape[:2] # im, hw_original, hw_resized
+ self.buffer.append(i)
+ if 1 < len(self.buffer) >= self.max_buffer_length: # prevent empty buffer
+ j = self.buffer.pop(0)
+ if self.cache != "ram":
+ self.ims[j], self.im_hw0[j], self.im_hw[j] = None, None, None
+
+ return im, (h0, w0), im.shape[:2]
+
+ return self.ims[i], self.im_hw0[i], self.im_hw[i]
+
+ def cache_images(self):
+ """Cache images to memory or disk."""
+ b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes
+ fcn, storage = (self.cache_images_to_disk, "Disk") if self.cache == "disk" else (self.load_image, "RAM")
+ with ThreadPool(NUM_THREADS) as pool:
+ results = pool.imap(fcn, range(self.ni))
+ pbar = TQDM(enumerate(results), total=self.ni, disable=LOCAL_RANK > 0)
+ for i, x in pbar:
+ if self.cache == "disk":
+ b += self.npy_files[i].stat().st_size
+ else: # 'ram'
+ self.ims[i], self.im_hw0[i], self.im_hw[i] = x # im, hw_orig, hw_resized = load_image(self, i)
+ b += self.ims[i].nbytes
+ pbar.desc = f"{self.prefix}Caching images ({b / gb:.1f}GB {storage})"
+ pbar.close()
+
+ def cache_images_to_disk(self, i):
+ """Saves an image as an *.npy file for faster loading."""
+ f = self.npy_files[i]
+ if not f.exists():
+ np.save(f.as_posix(), cv2.imread(self.im_files[i]), allow_pickle=False)
+
+ def check_cache_disk(self, safety_margin=0.5):
+ """Check image caching requirements vs available disk space."""
+ import shutil
+
+ b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes
+ n = min(self.ni, 30) # extrapolate from 30 random images
+ for _ in range(n):
+ im_file = random.choice(self.im_files)
+ im = cv2.imread(im_file)
+ if im is None:
+ continue
+ b += im.nbytes
+ if not os.access(Path(im_file).parent, os.W_OK):
+ self.cache = None
+ LOGGER.info(f"{self.prefix}Skipping caching images to disk, directory not writeable ⚠️")
+ return False
+ disk_required = b * self.ni / n * (1 + safety_margin) # bytes required to cache dataset to disk
+ total, used, free = shutil.disk_usage(Path(self.im_files[0]).parent)
+ if disk_required > free:
+ self.cache = None
+ LOGGER.info(
+ f"{self.prefix}{disk_required / gb:.1f}GB disk space required, "
+ f"with {int(safety_margin * 100)}% safety margin but only "
+ f"{free / gb:.1f}/{total / gb:.1f}GB free, not caching images to disk ⚠️"
+ )
+ return False
+ return True
+
+ def check_cache_ram(self, safety_margin=0.5):
+ """Check image caching requirements vs available memory."""
+ b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes
+ n = min(self.ni, 30) # extrapolate from 30 random images
+ for _ in range(n):
+ im = cv2.imread(random.choice(self.im_files)) # sample image
+ if im is None:
+ continue
+ ratio = self.imgsz / max(im.shape[0], im.shape[1]) # max(h, w) # ratio
+ b += im.nbytes * ratio**2
+ mem_required = b * self.ni / n * (1 + safety_margin) # GB required to cache dataset into RAM
+ mem = psutil.virtual_memory()
+ if mem_required > mem.available:
+ self.cache = None
+ LOGGER.info(
+ f"{self.prefix}{mem_required / gb:.1f}GB RAM required to cache images "
+ f"with {int(safety_margin * 100)}% safety margin but only "
+ f"{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, not caching images ⚠️"
+ )
+ return False
+ return True
+
+ def set_rectangle(self):
+ """Sets the shape of bounding boxes for YOLO detections as rectangles."""
+ bi = np.floor(np.arange(self.ni) / self.batch_size).astype(int) # batch index
+ nb = bi[-1] + 1 # number of batches
+
+ s = np.array([x.pop("shape") for x in self.labels]) # hw
+ ar = s[:, 0] / s[:, 1] # aspect ratio
+ irect = ar.argsort()
+ self.im_files = [self.im_files[i] for i in irect]
+ self.labels = [self.labels[i] for i in irect]
+ ar = ar[irect]
+
+ # Set training image shapes
+ shapes = [[1, 1]] * nb
+ for i in range(nb):
+ ari = ar[bi == i]
+ mini, maxi = ari.min(), ari.max()
+ if maxi < 1:
+ shapes[i] = [maxi, 1]
+ elif mini > 1:
+ shapes[i] = [1, 1 / mini]
+
+ self.batch_shapes = np.ceil(np.array(shapes) * self.imgsz / self.stride + self.pad).astype(int) * self.stride
+ self.batch = bi # batch index of image
+
+ def __getitem__(self, index):
+ """Returns transformed label information for given index."""
+ return self.transforms(self.get_image_and_label(index))
+
+ def get_image_and_label(self, index):
+ """Get and return label information from the dataset."""
+ label = deepcopy(self.labels[index]) # requires deepcopy() https://github.com/ultralytics/ultralytics/pull/1948
+ label.pop("shape", None) # shape is for rect, remove it
+ label["img"], label["ori_shape"], label["resized_shape"] = self.load_image(index)
+ label["ratio_pad"] = (
+ label["resized_shape"][0] / label["ori_shape"][0],
+ label["resized_shape"][1] / label["ori_shape"][1],
+ ) # for evaluation
+ if self.rect:
+ label["rect_shape"] = self.batch_shapes[self.batch[index]]
+ return self.update_labels_info(label)
+
+ def __len__(self):
+ """Returns the length of the labels list for the dataset."""
+ return len(self.labels)
+
+ def update_labels_info(self, label):
+ """Custom your label format here."""
+ return label
+
+ def build_transforms(self, hyp=None):
+ """
+ Users can customize augmentations here.
+
+ Example:
+ ```python
+ if self.augment:
+ # Training transforms
+ return Compose([])
+ else:
+ # Val transforms
+ return Compose([])
+ ```
+ """
+ raise NotImplementedError
+
+ def get_labels(self):
+ """
+ Users can customize their own format here.
+
+ Note:
+ Ensure output is a dictionary with the following keys:
+ ```python
+ dict(
+ im_file=im_file,
+ shape=shape, # format: (height, width)
+ cls=cls,
+ bboxes=bboxes, # xywh
+ segments=segments, # xy
+ keypoints=keypoints, # xy
+ normalized=True, # or False
+ bbox_format="xyxy", # or xywh, ltwh
+ )
+ ```
+ """
+ raise NotImplementedError
diff --git a/ultralytics/data/build.py b/ultralytics/data/build.py
new file mode 100644
index 0000000000000000000000000000000000000000..33b31ba4fb32ffed6c8ae88c6b92777c998ab765
--- /dev/null
+++ b/ultralytics/data/build.py
@@ -0,0 +1,215 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import os
+import random
+from pathlib import Path
+
+import numpy as np
+import torch
+from PIL import Image
+from torch.utils.data import dataloader, distributed
+
+from ultralytics.data.dataset import GroundingDataset, YOLODataset, YOLOMultiModalDataset
+from ultralytics.data.loaders import (
+ LOADERS,
+ LoadImagesAndVideos,
+ LoadPilAndNumpy,
+ LoadScreenshots,
+ LoadStreams,
+ LoadTensor,
+ SourceTypes,
+ autocast_list,
+)
+from ultralytics.data.utils import IMG_FORMATS, PIN_MEMORY, VID_FORMATS
+from ultralytics.utils import RANK, colorstr
+from ultralytics.utils.checks import check_file
+
+
+class InfiniteDataLoader(dataloader.DataLoader):
+ """
+ Dataloader that reuses workers.
+
+ Uses same syntax as vanilla DataLoader.
+ """
+
+ def __init__(self, *args, **kwargs):
+ """Dataloader that infinitely recycles workers, inherits from DataLoader."""
+ super().__init__(*args, **kwargs)
+ object.__setattr__(self, "batch_sampler", _RepeatSampler(self.batch_sampler))
+ self.iterator = super().__iter__()
+
+ def __len__(self):
+ """Returns the length of the batch sampler's sampler."""
+ return len(self.batch_sampler.sampler)
+
+ def __iter__(self):
+ """Creates a sampler that repeats indefinitely."""
+ for _ in range(len(self)):
+ yield next(self.iterator)
+
+ def __del__(self):
+ """Ensure that workers are terminated."""
+ if hasattr(self.iterator, "_workers"):
+ for w in self.iterator._workers: # force terminate
+ if w.is_alive():
+ w.terminate()
+ self.iterator._shutdown_workers() # cleanup
+
+ def reset(self):
+ """
+ Reset iterator.
+
+ This is useful when we want to modify settings of dataset while training.
+ """
+ self.iterator = self._get_iterator()
+
+
+class _RepeatSampler:
+ """
+ Sampler that repeats forever.
+
+ Args:
+ sampler (Dataset.sampler): The sampler to repeat.
+ """
+
+ def __init__(self, sampler):
+ """Initializes an object that repeats a given sampler indefinitely."""
+ self.sampler = sampler
+
+ def __iter__(self):
+ """Iterates over the 'sampler' and yields its contents."""
+ while True:
+ yield from iter(self.sampler)
+
+
+def seed_worker(worker_id): # noqa
+ """Set dataloader worker seed https://pytorch.org/docs/stable/notes/randomness.html#dataloader."""
+ worker_seed = torch.initial_seed() % 2**32
+ np.random.seed(worker_seed)
+ random.seed(worker_seed)
+
+
+def build_yolo_dataset(cfg, img_path, batch, data, mode="train", rect=False, stride=32, multi_modal=False):
+ """Build YOLO Dataset."""
+ dataset = YOLOMultiModalDataset if multi_modal else YOLODataset
+ return dataset(
+ img_path=img_path,
+ imgsz=cfg.imgsz,
+ batch_size=batch,
+ augment=mode == "train", # augmentation
+ hyp=cfg, # TODO: probably add a get_hyps_from_cfg function
+ rect=cfg.rect or rect, # rectangular batches
+ cache=cfg.cache or None,
+ single_cls=cfg.single_cls or False,
+ stride=int(stride),
+ pad=0.0 if mode == "train" else 0.5,
+ prefix=colorstr(f"{mode}: "),
+ task=cfg.task,
+ classes=cfg.classes,
+ data=data,
+ fraction=cfg.fraction if mode == "train" else 1.0,
+ )
+
+
+def build_grounding(cfg, img_path, json_file, batch, mode="train", rect=False, stride=32):
+ """Build YOLO Dataset."""
+ return GroundingDataset(
+ img_path=img_path,
+ json_file=json_file,
+ imgsz=cfg.imgsz,
+ batch_size=batch,
+ augment=mode == "train", # augmentation
+ hyp=cfg, # TODO: probably add a get_hyps_from_cfg function
+ rect=cfg.rect or rect, # rectangular batches
+ cache=cfg.cache or None,
+ single_cls=cfg.single_cls or False,
+ stride=int(stride),
+ pad=0.0 if mode == "train" else 0.5,
+ prefix=colorstr(f"{mode}: "),
+ task=cfg.task,
+ classes=cfg.classes,
+ fraction=cfg.fraction if mode == "train" else 1.0,
+ )
+
+
+def build_dataloader(dataset, batch, workers, shuffle=True, rank=-1):
+ """Return an InfiniteDataLoader or DataLoader for training or validation set."""
+ batch = min(batch, len(dataset))
+ nd = torch.cuda.device_count() # number of CUDA devices
+ nw = min(os.cpu_count() // max(nd, 1), workers) # number of workers
+ sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
+ generator = torch.Generator()
+ generator.manual_seed(6148914691236517205 + RANK)
+ return InfiniteDataLoader(
+ dataset=dataset,
+ batch_size=batch,
+ shuffle=shuffle and sampler is None,
+ num_workers=nw,
+ sampler=sampler,
+ pin_memory=PIN_MEMORY,
+ collate_fn=getattr(dataset, "collate_fn", None),
+ worker_init_fn=seed_worker,
+ generator=generator,
+ )
+
+
+def check_source(source):
+ """Check source type and return corresponding flag values."""
+ webcam, screenshot, from_img, in_memory, tensor = False, False, False, False, False
+ if isinstance(source, (str, int, Path)): # int for local usb camera
+ source = str(source)
+ is_file = Path(source).suffix[1:] in (IMG_FORMATS | VID_FORMATS)
+ is_url = source.lower().startswith(("https://", "http://", "rtsp://", "rtmp://", "tcp://"))
+ webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file)
+ screenshot = source.lower() == "screen"
+ if is_url and is_file:
+ source = check_file(source) # download
+ elif isinstance(source, LOADERS):
+ in_memory = True
+ elif isinstance(source, (list, tuple)):
+ source = autocast_list(source) # convert all list elements to PIL or np arrays
+ from_img = True
+ elif isinstance(source, (Image.Image, np.ndarray)):
+ from_img = True
+ elif isinstance(source, torch.Tensor):
+ tensor = True
+ else:
+ raise TypeError("Unsupported image type. For supported types see https://docs.ultralytics.com/modes/predict")
+
+ return source, webcam, screenshot, from_img, in_memory, tensor
+
+
+def load_inference_source(source=None, batch=1, vid_stride=1, buffer=False):
+ """
+ Loads an inference source for object detection and applies necessary transformations.
+
+ Args:
+ source (str, Path, Tensor, PIL.Image, np.ndarray): The input source for inference.
+ batch (int, optional): Batch size for dataloaders. Default is 1.
+ vid_stride (int, optional): The frame interval for video sources. Default is 1.
+ buffer (bool, optional): Determined whether stream frames will be buffered. Default is False.
+
+ Returns:
+ dataset (Dataset): A dataset object for the specified input source.
+ """
+ source, stream, screenshot, from_img, in_memory, tensor = check_source(source)
+ source_type = source.source_type if in_memory else SourceTypes(stream, screenshot, from_img, tensor)
+
+ # Dataloader
+ if tensor:
+ dataset = LoadTensor(source)
+ elif in_memory:
+ dataset = source
+ elif stream:
+ dataset = LoadStreams(source, vid_stride=vid_stride, buffer=buffer)
+ elif screenshot:
+ dataset = LoadScreenshots(source)
+ elif from_img:
+ dataset = LoadPilAndNumpy(source)
+ else:
+ dataset = LoadImagesAndVideos(source, batch=batch, vid_stride=vid_stride)
+
+ # Attach source types to the dataset
+ setattr(dataset, "source_type", source_type)
+
+ return dataset
diff --git a/ultralytics/data/converter.py b/ultralytics/data/converter.py
new file mode 100644
index 0000000000000000000000000000000000000000..05a316b4858a20e62c131e488e1b2f70cde26ed7
--- /dev/null
+++ b/ultralytics/data/converter.py
@@ -0,0 +1,702 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import json
+import random
+import shutil
+from collections import defaultdict
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from pathlib import Path
+
+import cv2
+import numpy as np
+from PIL import Image
+
+from ultralytics.utils import DATASETS_DIR, LOGGER, NUM_THREADS, TQDM
+from ultralytics.utils.downloads import download
+from ultralytics.utils.files import increment_path
+
+
+def coco91_to_coco80_class():
+ """
+ Converts 91-index COCO class IDs to 80-index COCO class IDs.
+
+ Returns:
+ (list): A list of 91 class IDs where the index represents the 80-index class ID and the value is the
+ corresponding 91-index class ID.
+ """
+ return [
+ 0,
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ None,
+ 11,
+ 12,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 20,
+ 21,
+ 22,
+ 23,
+ None,
+ 24,
+ 25,
+ None,
+ None,
+ 26,
+ 27,
+ 28,
+ 29,
+ 30,
+ 31,
+ 32,
+ 33,
+ 34,
+ 35,
+ 36,
+ 37,
+ 38,
+ 39,
+ None,
+ 40,
+ 41,
+ 42,
+ 43,
+ 44,
+ 45,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51,
+ 52,
+ 53,
+ 54,
+ 55,
+ 56,
+ 57,
+ 58,
+ 59,
+ None,
+ 60,
+ None,
+ None,
+ 61,
+ None,
+ 62,
+ 63,
+ 64,
+ 65,
+ 66,
+ 67,
+ 68,
+ 69,
+ 70,
+ 71,
+ 72,
+ None,
+ 73,
+ 74,
+ 75,
+ 76,
+ 77,
+ 78,
+ 79,
+ None,
+ ]
+
+
+def coco80_to_coco91_class():
+ r"""
+ Converts 80-index (val2014) to 91-index (paper).
+ For details see https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/.
+
+ Example:
+ ```python
+ import numpy as np
+
+ a = np.loadtxt("data/coco.names", dtype="str", delimiter="\n")
+ b = np.loadtxt("data/coco_paper.names", dtype="str", delimiter="\n")
+ x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco
+ x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet
+ ```
+ """
+ return [
+ 1,
+ 2,
+ 3,
+ 4,
+ 5,
+ 6,
+ 7,
+ 8,
+ 9,
+ 10,
+ 11,
+ 13,
+ 14,
+ 15,
+ 16,
+ 17,
+ 18,
+ 19,
+ 20,
+ 21,
+ 22,
+ 23,
+ 24,
+ 25,
+ 27,
+ 28,
+ 31,
+ 32,
+ 33,
+ 34,
+ 35,
+ 36,
+ 37,
+ 38,
+ 39,
+ 40,
+ 41,
+ 42,
+ 43,
+ 44,
+ 46,
+ 47,
+ 48,
+ 49,
+ 50,
+ 51,
+ 52,
+ 53,
+ 54,
+ 55,
+ 56,
+ 57,
+ 58,
+ 59,
+ 60,
+ 61,
+ 62,
+ 63,
+ 64,
+ 65,
+ 67,
+ 70,
+ 72,
+ 73,
+ 74,
+ 75,
+ 76,
+ 77,
+ 78,
+ 79,
+ 80,
+ 81,
+ 82,
+ 84,
+ 85,
+ 86,
+ 87,
+ 88,
+ 89,
+ 90,
+ ]
+
+
+def convert_coco(
+ labels_dir="../coco/annotations/",
+ save_dir="coco_converted/",
+ use_segments=False,
+ use_keypoints=False,
+ cls91to80=True,
+ lvis=False,
+):
+ """
+ Converts COCO dataset annotations to a YOLO annotation format suitable for training YOLO models.
+
+ Args:
+ labels_dir (str, optional): Path to directory containing COCO dataset annotation files.
+ save_dir (str, optional): Path to directory to save results to.
+ use_segments (bool, optional): Whether to include segmentation masks in the output.
+ use_keypoints (bool, optional): Whether to include keypoint annotations in the output.
+ cls91to80 (bool, optional): Whether to map 91 COCO class IDs to the corresponding 80 COCO class IDs.
+ lvis (bool, optional): Whether to convert data in lvis dataset way.
+
+ Example:
+ ```python
+ from ultralytics.data.converter import convert_coco
+
+ convert_coco("../datasets/coco/annotations/", use_segments=True, use_keypoints=False, cls91to80=False)
+ convert_coco(
+ "../datasets/lvis/annotations/", use_segments=True, use_keypoints=False, cls91to80=False, lvis=True
+ )
+ ```
+
+ Output:
+ Generates output files in the specified output directory.
+ """
+ # Create dataset directory
+ save_dir = increment_path(save_dir) # increment if save directory already exists
+ for p in save_dir / "labels", save_dir / "images":
+ p.mkdir(parents=True, exist_ok=True) # make dir
+
+ # Convert classes
+ coco80 = coco91_to_coco80_class()
+
+ # Import json
+ for json_file in sorted(Path(labels_dir).resolve().glob("*.json")):
+ lname = "" if lvis else json_file.stem.replace("instances_", "")
+ fn = Path(save_dir) / "labels" / lname # folder name
+ fn.mkdir(parents=True, exist_ok=True)
+ if lvis:
+ # NOTE: create folders for both train and val in advance,
+ # since LVIS val set contains images from COCO 2017 train in addition to the COCO 2017 val split.
+ (fn / "train2017").mkdir(parents=True, exist_ok=True)
+ (fn / "val2017").mkdir(parents=True, exist_ok=True)
+ with open(json_file, encoding="utf-8") as f:
+ data = json.load(f)
+
+ # Create image dict
+ images = {f"{x['id']:d}": x for x in data["images"]}
+ # Create image-annotations dict
+ imgToAnns = defaultdict(list)
+ for ann in data["annotations"]:
+ imgToAnns[ann["image_id"]].append(ann)
+
+ image_txt = []
+ # Write labels file
+ for img_id, anns in TQDM(imgToAnns.items(), desc=f"Annotations {json_file}"):
+ img = images[f"{img_id:d}"]
+ h, w = img["height"], img["width"]
+ f = str(Path(img["coco_url"]).relative_to("http://images.cocodataset.org")) if lvis else img["file_name"]
+ if lvis:
+ image_txt.append(str(Path("./images") / f))
+
+ bboxes = []
+ segments = []
+ keypoints = []
+ for ann in anns:
+ if ann.get("iscrowd", False):
+ continue
+ # The COCO box format is [top left x, top left y, width, height]
+ box = np.array(ann["bbox"], dtype=np.float64)
+ box[:2] += box[2:] / 2 # xy top-left corner to center
+ box[[0, 2]] /= w # normalize x
+ box[[1, 3]] /= h # normalize y
+ if box[2] <= 0 or box[3] <= 0: # if w <= 0 and h <= 0
+ continue
+
+ cls = coco80[ann["category_id"] - 1] if cls91to80 else ann["category_id"] - 1 # class
+ box = [cls] + box.tolist()
+ if box not in bboxes:
+ bboxes.append(box)
+ if use_segments and ann.get("segmentation") is not None:
+ if len(ann["segmentation"]) == 0:
+ segments.append([])
+ continue
+ elif len(ann["segmentation"]) > 1:
+ s = merge_multi_segment(ann["segmentation"])
+ s = (np.concatenate(s, axis=0) / np.array([w, h])).reshape(-1).tolist()
+ else:
+ s = [j for i in ann["segmentation"] for j in i] # all segments concatenated
+ s = (np.array(s).reshape(-1, 2) / np.array([w, h])).reshape(-1).tolist()
+ s = [cls] + s
+ segments.append(s)
+ if use_keypoints and ann.get("keypoints") is not None:
+ keypoints.append(
+ box + (np.array(ann["keypoints"]).reshape(-1, 3) / np.array([w, h, 1])).reshape(-1).tolist()
+ )
+
+ # Write
+ with open((fn / f).with_suffix(".txt"), "a") as file:
+ for i in range(len(bboxes)):
+ if use_keypoints:
+ line = (*(keypoints[i]),) # cls, box, keypoints
+ else:
+ line = (
+ *(segments[i] if use_segments and len(segments[i]) > 0 else bboxes[i]),
+ ) # cls, box or segments
+ file.write(("%g " * len(line)).rstrip() % line + "\n")
+
+ if lvis:
+ with open((Path(save_dir) / json_file.name.replace("lvis_v1_", "").replace(".json", ".txt")), "a") as f:
+ f.writelines(f"{line}\n" for line in image_txt)
+
+ LOGGER.info(f"{'LVIS' if lvis else 'COCO'} data converted successfully.\nResults saved to {save_dir.resolve()}")
+
+
+def convert_segment_masks_to_yolo_seg(masks_dir, output_dir, classes):
+ """
+ Converts a dataset of segmentation mask images to the YOLO segmentation format.
+
+ This function takes the directory containing the binary format mask images and converts them into YOLO segmentation format.
+ The converted masks are saved in the specified output directory.
+
+ Args:
+ masks_dir (str): The path to the directory where all mask images (png, jpg) are stored.
+ output_dir (str): The path to the directory where the converted YOLO segmentation masks will be stored.
+ classes (int): Total classes in the dataset i.e. for COCO classes=80
+
+ Example:
+ ```python
+ from ultralytics.data.converter import convert_segment_masks_to_yolo_seg
+
+ # The classes here is the total classes in the dataset, for COCO dataset we have 80 classes
+ convert_segment_masks_to_yolo_seg("path/to/masks_directory", "path/to/output/directory", classes=80)
+ ```
+
+ Notes:
+ The expected directory structure for the masks is:
+
+ - masks
+ ├─ mask_image_01.png or mask_image_01.jpg
+ ├─ mask_image_02.png or mask_image_02.jpg
+ ├─ mask_image_03.png or mask_image_03.jpg
+ └─ mask_image_04.png or mask_image_04.jpg
+
+ After execution, the labels will be organized in the following structure:
+
+ - output_dir
+ ├─ mask_yolo_01.txt
+ ├─ mask_yolo_02.txt
+ ├─ mask_yolo_03.txt
+ └─ mask_yolo_04.txt
+ """
+ pixel_to_class_mapping = {i + 1: i for i in range(classes)}
+ for mask_path in Path(masks_dir).iterdir():
+ if mask_path.suffix in {".png", ".jpg"}:
+ mask = cv2.imread(str(mask_path), cv2.IMREAD_GRAYSCALE) # Read the mask image in grayscale
+ img_height, img_width = mask.shape # Get image dimensions
+ LOGGER.info(f"Processing {mask_path} imgsz = {img_height} x {img_width}")
+
+ unique_values = np.unique(mask) # Get unique pixel values representing different classes
+ yolo_format_data = []
+
+ for value in unique_values:
+ if value == 0:
+ continue # Skip background
+ class_index = pixel_to_class_mapping.get(value, -1)
+ if class_index == -1:
+ LOGGER.warning(f"Unknown class for pixel value {value} in file {mask_path}, skipping.")
+ continue
+
+ # Create a binary mask for the current class and find contours
+ contours, _ = cv2.findContours(
+ (mask == value).astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
+ ) # Find contours
+
+ for contour in contours:
+ if len(contour) >= 3: # YOLO requires at least 3 points for a valid segmentation
+ contour = contour.squeeze() # Remove single-dimensional entries
+ yolo_format = [class_index]
+ for point in contour:
+ # Normalize the coordinates
+ yolo_format.append(round(point[0] / img_width, 6)) # Rounding to 6 decimal places
+ yolo_format.append(round(point[1] / img_height, 6))
+ yolo_format_data.append(yolo_format)
+ # Save Ultralytics YOLO format data to file
+ output_path = Path(output_dir) / f"{mask_path.stem}.txt"
+ with open(output_path, "w") as file:
+ for item in yolo_format_data:
+ line = " ".join(map(str, item))
+ file.write(line + "\n")
+ LOGGER.info(f"Processed and stored at {output_path} imgsz = {img_height} x {img_width}")
+
+
+def convert_dota_to_yolo_obb(dota_root_path: str):
+ """
+ Converts DOTA dataset annotations to YOLO OBB (Oriented Bounding Box) format.
+
+ The function processes images in the 'train' and 'val' folders of the DOTA dataset. For each image, it reads the
+ associated label from the original labels directory and writes new labels in YOLO OBB format to a new directory.
+
+ Args:
+ dota_root_path (str): The root directory path of the DOTA dataset.
+
+ Example:
+ ```python
+ from ultralytics.data.converter import convert_dota_to_yolo_obb
+
+ convert_dota_to_yolo_obb("path/to/DOTA")
+ ```
+
+ Notes:
+ The directory structure assumed for the DOTA dataset:
+
+ - DOTA
+ ├─ images
+ │ ├─ train
+ │ └─ val
+ └─ labels
+ ├─ train_original
+ └─ val_original
+
+ After execution, the function will organize the labels into:
+
+ - DOTA
+ └─ labels
+ ├─ train
+ └─ val
+ """
+ dota_root_path = Path(dota_root_path)
+
+ # Class names to indices mapping
+ class_mapping = {
+ "plane": 0,
+ "ship": 1,
+ "storage-tank": 2,
+ "baseball-diamond": 3,
+ "tennis-court": 4,
+ "basketball-court": 5,
+ "ground-track-field": 6,
+ "harbor": 7,
+ "bridge": 8,
+ "large-vehicle": 9,
+ "small-vehicle": 10,
+ "helicopter": 11,
+ "roundabout": 12,
+ "soccer-ball-field": 13,
+ "swimming-pool": 14,
+ "container-crane": 15,
+ "airport": 16,
+ "helipad": 17,
+ }
+
+ def convert_label(image_name, image_width, image_height, orig_label_dir, save_dir):
+ """Converts a single image's DOTA annotation to YOLO OBB format and saves it to a specified directory."""
+ orig_label_path = orig_label_dir / f"{image_name}.txt"
+ save_path = save_dir / f"{image_name}.txt"
+
+ with orig_label_path.open("r") as f, save_path.open("w") as g:
+ lines = f.readlines()
+ for line in lines:
+ parts = line.strip().split()
+ if len(parts) < 9:
+ continue
+ class_name = parts[8]
+ class_idx = class_mapping[class_name]
+ coords = [float(p) for p in parts[:8]]
+ normalized_coords = [
+ coords[i] / image_width if i % 2 == 0 else coords[i] / image_height for i in range(8)
+ ]
+ formatted_coords = [f"{coord:.6g}" for coord in normalized_coords]
+ g.write(f"{class_idx} {' '.join(formatted_coords)}\n")
+
+ for phase in ["train", "val"]:
+ image_dir = dota_root_path / "images" / phase
+ orig_label_dir = dota_root_path / "labels" / f"{phase}_original"
+ save_dir = dota_root_path / "labels" / phase
+
+ save_dir.mkdir(parents=True, exist_ok=True)
+
+ image_paths = list(image_dir.iterdir())
+ for image_path in TQDM(image_paths, desc=f"Processing {phase} images"):
+ if image_path.suffix != ".png":
+ continue
+ image_name_without_ext = image_path.stem
+ img = cv2.imread(str(image_path))
+ h, w = img.shape[:2]
+ convert_label(image_name_without_ext, w, h, orig_label_dir, save_dir)
+
+
+def min_index(arr1, arr2):
+ """
+ Find a pair of indexes with the shortest distance between two arrays of 2D points.
+
+ Args:
+ arr1 (np.ndarray): A NumPy array of shape (N, 2) representing N 2D points.
+ arr2 (np.ndarray): A NumPy array of shape (M, 2) representing M 2D points.
+
+ Returns:
+ (tuple): A tuple containing the indexes of the points with the shortest distance in arr1 and arr2 respectively.
+ """
+ dis = ((arr1[:, None, :] - arr2[None, :, :]) ** 2).sum(-1)
+ return np.unravel_index(np.argmin(dis, axis=None), dis.shape)
+
+
+def merge_multi_segment(segments):
+ """
+ Merge multiple segments into one list by connecting the coordinates with the minimum distance between each segment.
+ This function connects these coordinates with a thin line to merge all segments into one.
+
+ Args:
+ segments (List[List]): Original segmentations in COCO's JSON file.
+ Each element is a list of coordinates, like [segmentation1, segmentation2,...].
+
+ Returns:
+ s (List[np.ndarray]): A list of connected segments represented as NumPy arrays.
+ """
+ s = []
+ segments = [np.array(i).reshape(-1, 2) for i in segments]
+ idx_list = [[] for _ in range(len(segments))]
+
+ # Record the indexes with min distance between each segment
+ for i in range(1, len(segments)):
+ idx1, idx2 = min_index(segments[i - 1], segments[i])
+ idx_list[i - 1].append(idx1)
+ idx_list[i].append(idx2)
+
+ # Use two round to connect all the segments
+ for k in range(2):
+ # Forward connection
+ if k == 0:
+ for i, idx in enumerate(idx_list):
+ # Middle segments have two indexes, reverse the index of middle segments
+ if len(idx) == 2 and idx[0] > idx[1]:
+ idx = idx[::-1]
+ segments[i] = segments[i][::-1, :]
+
+ segments[i] = np.roll(segments[i], -idx[0], axis=0)
+ segments[i] = np.concatenate([segments[i], segments[i][:1]])
+ # Deal with the first segment and the last one
+ if i in {0, len(idx_list) - 1}:
+ s.append(segments[i])
+ else:
+ idx = [0, idx[1] - idx[0]]
+ s.append(segments[i][idx[0] : idx[1] + 1])
+
+ else:
+ for i in range(len(idx_list) - 1, -1, -1):
+ if i not in {0, len(idx_list) - 1}:
+ idx = idx_list[i]
+ nidx = abs(idx[1] - idx[0])
+ s.append(segments[i][nidx:])
+ return s
+
+
+def yolo_bbox2segment(im_dir, save_dir=None, sam_model="sam_b.pt", device=None):
+ """
+ Converts existing object detection dataset (bounding boxes) to segmentation dataset or oriented bounding box (OBB)
+ in YOLO format. Generates segmentation data using SAM auto-annotator as needed.
+
+ Args:
+ im_dir (str | Path): Path to image directory to convert.
+ save_dir (str | Path): Path to save the generated labels, labels will be saved
+ into `labels-segment` in the same directory level of `im_dir` if save_dir is None. Default: None.
+ sam_model (str): Segmentation model to use for intermediate segmentation data; optional.
+ device (int | str): The specific device to run SAM models. Default: None.
+
+ Notes:
+ The input directory structure assumed for dataset:
+
+ - im_dir
+ ├─ 001.jpg
+ ├─ ...
+ └─ NNN.jpg
+ - labels
+ ├─ 001.txt
+ ├─ ...
+ └─ NNN.txt
+ """
+ from ultralytics import SAM
+ from ultralytics.data import YOLODataset
+ from ultralytics.utils import LOGGER
+ from ultralytics.utils.ops import xywh2xyxy
+
+ # NOTE: add placeholder to pass class index check
+ dataset = YOLODataset(im_dir, data=dict(names=list(range(1000))))
+ if len(dataset.labels[0]["segments"]) > 0: # if it's segment data
+ LOGGER.info("Segmentation labels detected, no need to generate new ones!")
+ return
+
+ LOGGER.info("Detection labels detected, generating segment labels by SAM model!")
+ sam_model = SAM(sam_model)
+ for label in TQDM(dataset.labels, total=len(dataset.labels), desc="Generating segment labels"):
+ h, w = label["shape"]
+ boxes = label["bboxes"]
+ if len(boxes) == 0: # skip empty labels
+ continue
+ boxes[:, [0, 2]] *= w
+ boxes[:, [1, 3]] *= h
+ im = cv2.imread(label["im_file"])
+ sam_results = sam_model(im, bboxes=xywh2xyxy(boxes), verbose=False, save=False, device=device)
+ label["segments"] = sam_results[0].masks.xyn
+
+ save_dir = Path(save_dir) if save_dir else Path(im_dir).parent / "labels-segment"
+ save_dir.mkdir(parents=True, exist_ok=True)
+ for label in dataset.labels:
+ texts = []
+ lb_name = Path(label["im_file"]).with_suffix(".txt").name
+ txt_file = save_dir / lb_name
+ cls = label["cls"]
+ for i, s in enumerate(label["segments"]):
+ if len(s) == 0:
+ continue
+ line = (int(cls[i]), *s.reshape(-1))
+ texts.append(("%g " * len(line)).rstrip() % line)
+ with open(txt_file, "a") as f:
+ f.writelines(text + "\n" for text in texts)
+ LOGGER.info(f"Generated segment labels saved in {save_dir}")
+
+
+def create_synthetic_coco_dataset():
+ """
+ Creates a synthetic COCO dataset with random images based on filenames from label lists.
+
+ This function downloads COCO labels, reads image filenames from label list files,
+ creates synthetic images for train2017 and val2017 subsets, and organizes
+ them in the COCO dataset structure. It uses multithreading to generate images efficiently.
+
+ Examples:
+ >>> from ultralytics.data.converter import create_synthetic_coco_dataset
+ >>> create_synthetic_coco_dataset()
+
+ Notes:
+ - Requires internet connection to download label files.
+ - Generates random RGB images of varying sizes (480x480 to 640x640 pixels).
+ - Existing test2017 directory is removed as it's not needed.
+ - Reads image filenames from train2017.txt and val2017.txt files.
+ """
+
+ def create_synthetic_image(image_file):
+ """Generates synthetic images with random sizes and colors for dataset augmentation or testing purposes."""
+ if not image_file.exists():
+ size = (random.randint(480, 640), random.randint(480, 640))
+ Image.new(
+ "RGB",
+ size=size,
+ color=(random.randint(0, 255), random.randint(0, 255), random.randint(0, 255)),
+ ).save(image_file)
+
+ # Download labels
+ dir = DATASETS_DIR / "coco"
+ url = "https://github.com/ultralytics/assets/releases/download/v0.0.0/"
+ label_zip = "coco2017labels-segments.zip"
+ download([url + label_zip], dir=dir.parent)
+
+ # Create synthetic images
+ shutil.rmtree(dir / "labels" / "test2017", ignore_errors=True) # Remove test2017 directory as not needed
+ with ThreadPoolExecutor(max_workers=NUM_THREADS) as executor:
+ for subset in ["train2017", "val2017"]:
+ subset_dir = dir / "images" / subset
+ subset_dir.mkdir(parents=True, exist_ok=True)
+
+ # Read image filenames from label list file
+ label_list_file = dir / f"{subset}.txt"
+ if label_list_file.exists():
+ with open(label_list_file) as f:
+ image_files = [dir / line.strip() for line in f]
+
+ # Submit all tasks
+ futures = [executor.submit(create_synthetic_image, image_file) for image_file in image_files]
+ for _ in TQDM(as_completed(futures), total=len(futures), desc=f"Generating images for {subset}"):
+ pass # The actual work is done in the background
+ else:
+ print(f"Warning: Labels file {label_list_file} does not exist. Skipping image creation for {subset}.")
+
+ print("Synthetic COCO dataset created successfully.")
diff --git a/ultralytics/data/dataset.py b/ultralytics/data/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..1817c35c50b9af954b79707c922e0ecd22d50130
--- /dev/null
+++ b/ultralytics/data/dataset.py
@@ -0,0 +1,521 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import json
+from collections import defaultdict
+from itertools import repeat
+from multiprocessing.pool import ThreadPool
+from pathlib import Path
+
+import cv2
+import numpy as np
+import torch
+from PIL import Image
+from torch.utils.data import ConcatDataset
+
+from ultralytics.utils import LOCAL_RANK, NUM_THREADS, TQDM, colorstr
+from ultralytics.utils.ops import resample_segments
+from ultralytics.utils.torch_utils import TORCHVISION_0_18
+
+from .augment import (
+ Compose,
+ Format,
+ Instances,
+ LetterBox,
+ RandomLoadText,
+ classify_augmentations,
+ classify_transforms,
+ v8_transforms,
+)
+from .base import BaseDataset
+from .utils import (
+ HELP_URL,
+ LOGGER,
+ get_hash,
+ img2label_paths,
+ load_dataset_cache_file,
+ save_dataset_cache_file,
+ verify_image,
+ verify_image_label,
+)
+
+# Ultralytics dataset *.cache version, >= 1.0.0 for YOLOv8
+DATASET_CACHE_VERSION = "1.0.3"
+
+
+class YOLODataset(BaseDataset):
+ """
+ Dataset class for loading object detection and/or segmentation labels in YOLO format.
+
+ Args:
+ data (dict, optional): A dataset YAML dictionary. Defaults to None.
+ task (str): An explicit arg to point current task, Defaults to 'detect'.
+
+ Returns:
+ (torch.utils.data.Dataset): A PyTorch dataset object that can be used for training an object detection model.
+ """
+
+ def __init__(self, *args, data=None, task="detect", **kwargs):
+ """Initializes the YOLODataset with optional configurations for segments and keypoints."""
+ self.use_segments = task == "segment"
+ self.use_keypoints = task == "pose"
+ self.use_obb = task == "obb"
+ self.data = data
+ assert not (self.use_segments and self.use_keypoints), "Can not use both segments and keypoints."
+ super().__init__(*args, **kwargs)
+
+ def cache_labels(self, path=Path("./labels.cache")):
+ """
+ Cache dataset labels, check images and read shapes.
+
+ Args:
+ path (Path): Path where to save the cache file. Default is Path("./labels.cache").
+
+ Returns:
+ (dict): labels.
+ """
+ x = {"labels": []}
+ nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages
+ desc = f"{self.prefix}Scanning {path.parent / path.stem}..."
+ total = len(self.im_files)
+ nkpt, ndim = self.data.get("kpt_shape", (0, 0))
+ if self.use_keypoints and (nkpt <= 0 or ndim not in {2, 3}):
+ raise ValueError(
+ "'kpt_shape' in data.yaml missing or incorrect. Should be a list with [number of "
+ "keypoints, number of dims (2 for x,y or 3 for x,y,visible)], i.e. 'kpt_shape: [17, 3]'"
+ )
+ with ThreadPool(NUM_THREADS) as pool:
+ results = pool.imap(
+ func=verify_image_label,
+ iterable=zip(
+ self.im_files,
+ self.label_files,
+ repeat(self.prefix),
+ repeat(self.use_keypoints),
+ repeat(len(self.data["names"])),
+ repeat(nkpt),
+ repeat(ndim),
+ ),
+ )
+ pbar = TQDM(results, desc=desc, total=total)
+ for im_file, lb, shape, segments, keypoint, nm_f, nf_f, ne_f, nc_f, msg in pbar:
+ nm += nm_f
+ nf += nf_f
+ ne += ne_f
+ nc += nc_f
+ if im_file:
+ x["labels"].append(
+ {
+ "im_file": im_file,
+ "shape": shape,
+ "cls": lb[:, 0:1], # n, 1
+ "bboxes": lb[:, 1:], # n, 4
+ "segments": segments,
+ "keypoints": keypoint,
+ "normalized": True,
+ "bbox_format": "xywh",
+ }
+ )
+ if msg:
+ msgs.append(msg)
+ pbar.desc = f"{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt"
+ pbar.close()
+
+ if msgs:
+ LOGGER.info("\n".join(msgs))
+ if nf == 0:
+ LOGGER.warning(f"{self.prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}")
+ x["hash"] = get_hash(self.label_files + self.im_files)
+ x["results"] = nf, nm, ne, nc, len(self.im_files)
+ x["msgs"] = msgs # warnings
+ save_dataset_cache_file(self.prefix, path, x, DATASET_CACHE_VERSION)
+ return x
+
+ def get_labels(self):
+ """Returns dictionary of labels for YOLO training."""
+ self.label_files = img2label_paths(self.im_files)
+ cache_path = Path(self.label_files[0]).parent.with_suffix(".cache")
+ try:
+ cache, exists = load_dataset_cache_file(cache_path), True # attempt to load a *.cache file
+ assert cache["version"] == DATASET_CACHE_VERSION # matches current version
+ assert cache["hash"] == get_hash(self.label_files + self.im_files) # identical hash
+ except (FileNotFoundError, AssertionError, AttributeError):
+ cache, exists = self.cache_labels(cache_path), False # run cache ops
+
+ # Display cache
+ nf, nm, ne, nc, n = cache.pop("results") # found, missing, empty, corrupt, total
+ if exists and LOCAL_RANK in {-1, 0}:
+ d = f"Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt"
+ TQDM(None, desc=self.prefix + d, total=n, initial=n) # display results
+ if cache["msgs"]:
+ LOGGER.info("\n".join(cache["msgs"])) # display warnings
+
+ # Read cache
+ [cache.pop(k) for k in ("hash", "version", "msgs")] # remove items
+ labels = cache["labels"]
+ if not labels:
+ LOGGER.warning(f"WARNING ⚠️ No images found in {cache_path}, training may not work correctly. {HELP_URL}")
+ self.im_files = [lb["im_file"] for lb in labels] # update im_files
+
+ # Check if the dataset is all boxes or all segments
+ lengths = ((len(lb["cls"]), len(lb["bboxes"]), len(lb["segments"])) for lb in labels)
+ len_cls, len_boxes, len_segments = (sum(x) for x in zip(*lengths))
+ if len_segments and len_boxes != len_segments:
+ LOGGER.warning(
+ f"WARNING ⚠️ Box and segment counts should be equal, but got len(segments) = {len_segments}, "
+ f"len(boxes) = {len_boxes}. To resolve this only boxes will be used and all segments will be removed. "
+ "To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset."
+ )
+ for lb in labels:
+ lb["segments"] = []
+ if len_cls == 0:
+ LOGGER.warning(f"WARNING ⚠️ No labels found in {cache_path}, training may not work correctly. {HELP_URL}")
+ return labels
+
+ def build_transforms(self, hyp=None):
+ """Builds and appends transforms to the list."""
+ if self.augment:
+ hyp.mosaic = hyp.mosaic if self.augment and not self.rect else 0.0
+ hyp.mixup = hyp.mixup if self.augment and not self.rect else 0.0
+ transforms = v8_transforms(self, self.imgsz, hyp)
+ else:
+ transforms = Compose([LetterBox(new_shape=(self.imgsz, self.imgsz), scaleup=False)])
+ transforms.append(
+ Format(
+ bbox_format="xywh",
+ normalize=True,
+ return_mask=self.use_segments,
+ return_keypoint=self.use_keypoints,
+ return_obb=self.use_obb,
+ batch_idx=True,
+ mask_ratio=hyp.mask_ratio,
+ mask_overlap=hyp.overlap_mask,
+ bgr=hyp.bgr if self.augment else 0.0, # only affect training.
+ )
+ )
+ return transforms
+
+ def close_mosaic(self, hyp):
+ """Sets mosaic, copy_paste and mixup options to 0.0 and builds transformations."""
+ hyp.mosaic = 0.0 # set mosaic ratio=0.0
+ hyp.copy_paste = 0.0 # keep the same behavior as previous v8 close-mosaic
+ hyp.mixup = 0.0 # keep the same behavior as previous v8 close-mosaic
+ self.transforms = self.build_transforms(hyp)
+
+ def update_labels_info(self, label):
+ """
+ Custom your label format here.
+
+ Note:
+ cls is not with bboxes now, classification and semantic segmentation need an independent cls label
+ Can also support classification and semantic segmentation by adding or removing dict keys there.
+ """
+ bboxes = label.pop("bboxes")
+ segments = label.pop("segments", [])
+ keypoints = label.pop("keypoints", None)
+ bbox_format = label.pop("bbox_format")
+ normalized = label.pop("normalized")
+
+ # NOTE: do NOT resample oriented boxes
+ segment_resamples = 100 if self.use_obb else 1000
+ if len(segments) > 0:
+ # make sure segments interpolate correctly if original length is greater than segment_resamples
+ max_len = max(len(s) for s in segments)
+ segment_resamples = (max_len + 1) if segment_resamples < max_len else segment_resamples
+ # list[np.array(segment_resamples, 2)] * num_samples
+ segments = np.stack(resample_segments(segments, n=segment_resamples), axis=0)
+ else:
+ segments = np.zeros((0, segment_resamples, 2), dtype=np.float32)
+ label["instances"] = Instances(bboxes, segments, keypoints, bbox_format=bbox_format, normalized=normalized)
+ return label
+
+ @staticmethod
+ def collate_fn(batch):
+ """Collates data samples into batches."""
+ new_batch = {}
+ keys = batch[0].keys()
+ values = list(zip(*[list(b.values()) for b in batch]))
+ for i, k in enumerate(keys):
+ value = values[i]
+ if k == "img":
+ value = torch.stack(value, 0)
+ if k in {"masks", "keypoints", "bboxes", "cls", "segments", "obb"}:
+ value = torch.cat(value, 0)
+ new_batch[k] = value
+ new_batch["batch_idx"] = list(new_batch["batch_idx"])
+ for i in range(len(new_batch["batch_idx"])):
+ new_batch["batch_idx"][i] += i # add target image index for build_targets()
+ new_batch["batch_idx"] = torch.cat(new_batch["batch_idx"], 0)
+ return new_batch
+
+
+class YOLOMultiModalDataset(YOLODataset):
+ """
+ Dataset class for loading object detection and/or segmentation labels in YOLO format.
+
+ Args:
+ data (dict, optional): A dataset YAML dictionary. Defaults to None.
+ task (str): An explicit arg to point current task, Defaults to 'detect'.
+
+ Returns:
+ (torch.utils.data.Dataset): A PyTorch dataset object that can be used for training an object detection model.
+ """
+
+ def __init__(self, *args, data=None, task="detect", **kwargs):
+ """Initializes a dataset object for object detection tasks with optional specifications."""
+ super().__init__(*args, data=data, task=task, **kwargs)
+
+ def update_labels_info(self, label):
+ """Add texts information for multi-modal model training."""
+ labels = super().update_labels_info(label)
+ # NOTE: some categories are concatenated with its synonyms by `/`.
+ labels["texts"] = [v.split("/") for _, v in self.data["names"].items()]
+ return labels
+
+ def build_transforms(self, hyp=None):
+ """Enhances data transformations with optional text augmentation for multi-modal training."""
+ transforms = super().build_transforms(hyp)
+ if self.augment:
+ # NOTE: hard-coded the args for now.
+ transforms.insert(-1, RandomLoadText(max_samples=min(self.data["nc"], 80), padding=True))
+ return transforms
+
+
+class GroundingDataset(YOLODataset):
+ """Handles object detection tasks by loading annotations from a specified JSON file, supporting YOLO format."""
+
+ def __init__(self, *args, task="detect", json_file, **kwargs):
+ """Initializes a GroundingDataset for object detection, loading annotations from a specified JSON file."""
+ assert task == "detect", "`GroundingDataset` only support `detect` task for now!"
+ self.json_file = json_file
+ super().__init__(*args, task=task, data={}, **kwargs)
+
+ def get_img_files(self, img_path):
+ """The image files would be read in `get_labels` function, return empty list here."""
+ return []
+
+ def get_labels(self):
+ """Loads annotations from a JSON file, filters, and normalizes bounding boxes for each image."""
+ labels = []
+ LOGGER.info("Loading annotation file...")
+ with open(self.json_file) as f:
+ annotations = json.load(f)
+ images = {f"{x['id']:d}": x for x in annotations["images"]}
+ img_to_anns = defaultdict(list)
+ for ann in annotations["annotations"]:
+ img_to_anns[ann["image_id"]].append(ann)
+ for img_id, anns in TQDM(img_to_anns.items(), desc=f"Reading annotations {self.json_file}"):
+ img = images[f"{img_id:d}"]
+ h, w, f = img["height"], img["width"], img["file_name"]
+ im_file = Path(self.img_path) / f
+ if not im_file.exists():
+ continue
+ self.im_files.append(str(im_file))
+ bboxes = []
+ cat2id = {}
+ texts = []
+ for ann in anns:
+ if ann["iscrowd"]:
+ continue
+ box = np.array(ann["bbox"], dtype=np.float32)
+ box[:2] += box[2:] / 2
+ box[[0, 2]] /= float(w)
+ box[[1, 3]] /= float(h)
+ if box[2] <= 0 or box[3] <= 0:
+ continue
+
+ caption = img["caption"]
+ cat_name = " ".join([caption[t[0] : t[1]] for t in ann["tokens_positive"]])
+ if cat_name not in cat2id:
+ cat2id[cat_name] = len(cat2id)
+ texts.append([cat_name])
+ cls = cat2id[cat_name] # class
+ box = [cls] + box.tolist()
+ if box not in bboxes:
+ bboxes.append(box)
+ lb = np.array(bboxes, dtype=np.float32) if len(bboxes) else np.zeros((0, 5), dtype=np.float32)
+ labels.append(
+ {
+ "im_file": im_file,
+ "shape": (h, w),
+ "cls": lb[:, 0:1], # n, 1
+ "bboxes": lb[:, 1:], # n, 4
+ "normalized": True,
+ "bbox_format": "xywh",
+ "texts": texts,
+ }
+ )
+ return labels
+
+ def build_transforms(self, hyp=None):
+ """Configures augmentations for training with optional text loading; `hyp` adjusts augmentation intensity."""
+ transforms = super().build_transforms(hyp)
+ if self.augment:
+ # NOTE: hard-coded the args for now.
+ transforms.insert(-1, RandomLoadText(max_samples=80, padding=True))
+ return transforms
+
+
+class YOLOConcatDataset(ConcatDataset):
+ """
+ Dataset as a concatenation of multiple datasets.
+
+ This class is useful to assemble different existing datasets.
+ """
+
+ @staticmethod
+ def collate_fn(batch):
+ """Collates data samples into batches."""
+ return YOLODataset.collate_fn(batch)
+
+
+# TODO: support semantic segmentation
+class SemanticDataset(BaseDataset):
+ """
+ Semantic Segmentation Dataset.
+
+ This class is responsible for handling datasets used for semantic segmentation tasks. It inherits functionalities
+ from the BaseDataset class.
+
+ Note:
+ This class is currently a placeholder and needs to be populated with methods and attributes for supporting
+ semantic segmentation tasks.
+ """
+
+ def __init__(self):
+ """Initialize a SemanticDataset object."""
+ super().__init__()
+
+
+class ClassificationDataset:
+ """
+ Extends torchvision ImageFolder to support YOLO classification tasks, offering functionalities like image
+ augmentation, caching, and verification. It's designed to efficiently handle large datasets for training deep
+ learning models, with optional image transformations and caching mechanisms to speed up training.
+
+ This class allows for augmentations using both torchvision and Albumentations libraries, and supports caching images
+ in RAM or on disk to reduce IO overhead during training. Additionally, it implements a robust verification process
+ to ensure data integrity and consistency.
+
+ Attributes:
+ cache_ram (bool): Indicates if caching in RAM is enabled.
+ cache_disk (bool): Indicates if caching on disk is enabled.
+ samples (list): A list of tuples, each containing the path to an image, its class index, path to its .npy cache
+ file (if caching on disk), and optionally the loaded image array (if caching in RAM).
+ torch_transforms (callable): PyTorch transforms to be applied to the images.
+ """
+
+ def __init__(self, root, args, augment=False, prefix=""):
+ """
+ Initialize YOLO object with root, image size, augmentations, and cache settings.
+
+ Args:
+ root (str): Path to the dataset directory where images are stored in a class-specific folder structure.
+ args (Namespace): Configuration containing dataset-related settings such as image size, augmentation
+ parameters, and cache settings. It includes attributes like `imgsz` (image size), `fraction` (fraction
+ of data to use), `scale`, `fliplr`, `flipud`, `cache` (disk or RAM caching for faster training),
+ `auto_augment`, `hsv_h`, `hsv_s`, `hsv_v`, and `crop_fraction`.
+ augment (bool, optional): Whether to apply augmentations to the dataset. Default is False.
+ prefix (str, optional): Prefix for logging and cache filenames, aiding in dataset identification and
+ debugging. Default is an empty string.
+ """
+ import torchvision # scope for faster 'import ultralytics'
+
+ # Base class assigned as attribute rather than used as base class to allow for scoping slow torchvision import
+ if TORCHVISION_0_18: # 'allow_empty' argument first introduced in torchvision 0.18
+ self.base = torchvision.datasets.ImageFolder(root=root, allow_empty=True)
+ else:
+ self.base = torchvision.datasets.ImageFolder(root=root)
+ self.samples = self.base.samples
+ self.root = self.base.root
+
+ # Initialize attributes
+ if augment and args.fraction < 1.0: # reduce training fraction
+ self.samples = self.samples[: round(len(self.samples) * args.fraction)]
+ self.prefix = colorstr(f"{prefix}: ") if prefix else ""
+ self.cache_ram = args.cache is True or str(args.cache).lower() == "ram" # cache images into RAM
+ if self.cache_ram:
+ LOGGER.warning(
+ "WARNING ⚠️ Classification `cache_ram` training has known memory leak in "
+ "https://github.com/ultralytics/ultralytics/issues/9824, setting `cache_ram=False`."
+ )
+ self.cache_ram = False
+ self.cache_disk = str(args.cache).lower() == "disk" # cache images on hard drive as uncompressed *.npy files
+ self.samples = self.verify_images() # filter out bad images
+ self.samples = [list(x) + [Path(x[0]).with_suffix(".npy"), None] for x in self.samples] # file, index, npy, im
+ scale = (1.0 - args.scale, 1.0) # (0.08, 1.0)
+ self.torch_transforms = (
+ classify_augmentations(
+ size=args.imgsz,
+ scale=scale,
+ hflip=args.fliplr,
+ vflip=args.flipud,
+ erasing=args.erasing,
+ auto_augment=args.auto_augment,
+ hsv_h=args.hsv_h,
+ hsv_s=args.hsv_s,
+ hsv_v=args.hsv_v,
+ )
+ if augment
+ else classify_transforms(size=args.imgsz, crop_fraction=args.crop_fraction)
+ )
+
+ def __getitem__(self, i):
+ """Returns subset of data and targets corresponding to given indices."""
+ f, j, fn, im = self.samples[i] # filename, index, filename.with_suffix('.npy'), image
+ if self.cache_ram:
+ if im is None: # Warning: two separate if statements required here, do not combine this with previous line
+ im = self.samples[i][3] = cv2.imread(f)
+ elif self.cache_disk:
+ if not fn.exists(): # load npy
+ np.save(fn.as_posix(), cv2.imread(f), allow_pickle=False)
+ im = np.load(fn)
+ else: # read image
+ im = cv2.imread(f) # BGR
+ # Convert NumPy array to PIL image
+ im = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB))
+ sample = self.torch_transforms(im)
+ return {"img": sample, "cls": j}
+
+ def __len__(self) -> int:
+ """Return the total number of samples in the dataset."""
+ return len(self.samples)
+
+ def verify_images(self):
+ """Verify all images in dataset."""
+ desc = f"{self.prefix}Scanning {self.root}..."
+ path = Path(self.root).with_suffix(".cache") # *.cache file path
+
+ try:
+ cache = load_dataset_cache_file(path) # attempt to load a *.cache file
+ assert cache["version"] == DATASET_CACHE_VERSION # matches current version
+ assert cache["hash"] == get_hash([x[0] for x in self.samples]) # identical hash
+ nf, nc, n, samples = cache.pop("results") # found, missing, empty, corrupt, total
+ if LOCAL_RANK in {-1, 0}:
+ d = f"{desc} {nf} images, {nc} corrupt"
+ TQDM(None, desc=d, total=n, initial=n)
+ if cache["msgs"]:
+ LOGGER.info("\n".join(cache["msgs"])) # display warnings
+ return samples
+
+ except (FileNotFoundError, AssertionError, AttributeError):
+ # Run scan if *.cache retrieval failed
+ nf, nc, msgs, samples, x = 0, 0, [], [], {}
+ with ThreadPool(NUM_THREADS) as pool:
+ results = pool.imap(func=verify_image, iterable=zip(self.samples, repeat(self.prefix)))
+ pbar = TQDM(results, desc=desc, total=len(self.samples))
+ for sample, nf_f, nc_f, msg in pbar:
+ if nf_f:
+ samples.append(sample)
+ if msg:
+ msgs.append(msg)
+ nf += nf_f
+ nc += nc_f
+ pbar.desc = f"{desc} {nf} images, {nc} corrupt"
+ pbar.close()
+ if msgs:
+ LOGGER.info("\n".join(msgs))
+ x["hash"] = get_hash([x[0] for x in self.samples])
+ x["results"] = nf, nc, len(samples), samples
+ x["msgs"] = msgs # warnings
+ save_dataset_cache_file(self.prefix, path, x, DATASET_CACHE_VERSION)
+ return samples
diff --git a/ultralytics/data/loaders.py b/ultralytics/data/loaders.py
new file mode 100644
index 0000000000000000000000000000000000000000..3a04bb0383d9ea8d21426327b0a1d309dd70f607
--- /dev/null
+++ b/ultralytics/data/loaders.py
@@ -0,0 +1,658 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+import glob
+import math
+import os
+import time
+from dataclasses import dataclass
+from pathlib import Path
+from threading import Thread
+from urllib.parse import urlparse
+
+import cv2
+import numpy as np
+import requests
+import torch
+from PIL import Image
+
+from ultralytics.data.utils import FORMATS_HELP_MSG, IMG_FORMATS, VID_FORMATS
+from ultralytics.utils import IS_COLAB, IS_KAGGLE, LOGGER, ops
+from ultralytics.utils.checks import check_requirements
+from ultralytics.utils.patches import imread
+
+
+@dataclass
+class SourceTypes:
+ """
+ Class to represent various types of input sources for predictions.
+
+ This class uses dataclass to define boolean flags for different types of input sources that can be used for
+ making predictions with YOLO models.
+
+ Attributes:
+ stream (bool): Flag indicating if the input source is a video stream.
+ screenshot (bool): Flag indicating if the input source is a screenshot.
+ from_img (bool): Flag indicating if the input source is an image file.
+
+ Examples:
+ >>> source_types = SourceTypes(stream=True, screenshot=False, from_img=False)
+ >>> print(source_types.stream)
+ True
+ >>> print(source_types.from_img)
+ False
+ """
+
+ stream: bool = False
+ screenshot: bool = False
+ from_img: bool = False
+ tensor: bool = False
+
+
+class LoadStreams:
+ """
+ Stream Loader for various types of video streams.
+
+ Supports RTSP, RTMP, HTTP, and TCP streams. This class handles the loading and processing of multiple video
+ streams simultaneously, making it suitable for real-time video analysis tasks.
+
+ Attributes:
+ sources (List[str]): The source input paths or URLs for the video streams.
+ vid_stride (int): Video frame-rate stride.
+ buffer (bool): Whether to buffer input streams.
+ running (bool): Flag to indicate if the streaming thread is running.
+ mode (str): Set to 'stream' indicating real-time capture.
+ imgs (List[List[np.ndarray]]): List of image frames for each stream.
+ fps (List[float]): List of FPS for each stream.
+ frames (List[int]): List of total frames for each stream.
+ threads (List[Thread]): List of threads for each stream.
+ shape (List[Tuple[int, int, int]]): List of shapes for each stream.
+ caps (List[cv2.VideoCapture]): List of cv2.VideoCapture objects for each stream.
+ bs (int): Batch size for processing.
+
+ Methods:
+ update: Read stream frames in daemon thread.
+ close: Close stream loader and release resources.
+ __iter__: Returns an iterator object for the class.
+ __next__: Returns source paths, transformed, and original images for processing.
+ __len__: Return the length of the sources object.
+
+ Examples:
+ >>> stream_loader = LoadStreams("rtsp://example.com/stream1.mp4")
+ >>> for sources, imgs, _ in stream_loader:
+ ... # Process the images
+ ... pass
+ >>> stream_loader.close()
+
+ Notes:
+ - The class uses threading to efficiently load frames from multiple streams simultaneously.
+ - It automatically handles YouTube links, converting them to the best available stream URL.
+ - The class implements a buffer system to manage frame storage and retrieval.
+ """
+
+ def __init__(self, sources="file.streams", vid_stride=1, buffer=False):
+ """Initialize stream loader for multiple video sources, supporting various stream types."""
+ torch.backends.cudnn.benchmark = True # faster for fixed-size inference
+ self.buffer = buffer # buffer input streams
+ self.running = True # running flag for Thread
+ self.mode = "stream"
+ self.vid_stride = vid_stride # video frame-rate stride
+
+ sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources]
+ n = len(sources)
+ self.bs = n
+ self.fps = [0] * n # frames per second
+ self.frames = [0] * n
+ self.threads = [None] * n
+ self.caps = [None] * n # video capture objects
+ self.imgs = [[] for _ in range(n)] # images
+ self.shape = [[] for _ in range(n)] # image shapes
+ self.sources = [ops.clean_str(x) for x in sources] # clean source names for later
+ for i, s in enumerate(sources): # index, source
+ # Start thread to read frames from video stream
+ st = f"{i + 1}/{n}: {s}... "
+ if urlparse(s).hostname in {"www.youtube.com", "youtube.com", "youtu.be"}: # if source is YouTube video
+ # YouTube format i.e. 'https://www.youtube.com/watch?v=Jsn8D3aC840' or 'https://youtu.be/Jsn8D3aC840'
+ s = get_best_youtube_url(s)
+ s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam
+ if s == 0 and (IS_COLAB or IS_KAGGLE):
+ raise NotImplementedError(
+ "'source=0' webcam not supported in Colab and Kaggle notebooks. "
+ "Try running 'source=0' in a local environment."
+ )
+ self.caps[i] = cv2.VideoCapture(s) # store video capture object
+ if not self.caps[i].isOpened():
+ raise ConnectionError(f"{st}Failed to open {s}")
+ w = int(self.caps[i].get(cv2.CAP_PROP_FRAME_WIDTH))
+ h = int(self.caps[i].get(cv2.CAP_PROP_FRAME_HEIGHT))
+ fps = self.caps[i].get(cv2.CAP_PROP_FPS) # warning: may return 0 or nan
+ self.frames[i] = max(int(self.caps[i].get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float(
+ "inf"
+ ) # infinite stream fallback
+ self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30 # 30 FPS fallback
+
+ success, im = self.caps[i].read() # guarantee first frame
+ if not success or im is None:
+ raise ConnectionError(f"{st}Failed to read images from {s}")
+ self.imgs[i].append(im)
+ self.shape[i] = im.shape
+ self.threads[i] = Thread(target=self.update, args=([i, self.caps[i], s]), daemon=True)
+ LOGGER.info(f"{st}Success ✅ ({self.frames[i]} frames of shape {w}x{h} at {self.fps[i]:.2f} FPS)")
+ self.threads[i].start()
+ LOGGER.info("") # newline
+
+ def update(self, i, cap, stream):
+ """Read stream frames in daemon thread and update image buffer."""
+ n, f = 0, self.frames[i] # frame number, frame array
+ while self.running and cap.isOpened() and n < (f - 1):
+ if len(self.imgs[i]) < 30: # keep a <=30-image buffer
+ n += 1
+ cap.grab() # .read() = .grab() followed by .retrieve()
+ if n % self.vid_stride == 0:
+ success, im = cap.retrieve()
+ if not success:
+ im = np.zeros(self.shape[i], dtype=np.uint8)
+ LOGGER.warning("WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.")
+ cap.open(stream) # re-open stream if signal was lost
+ if self.buffer:
+ self.imgs[i].append(im)
+ else:
+ self.imgs[i] = [im]
+ else:
+ time.sleep(0.01) # wait until the buffer is empty
+
+ def close(self):
+ """Terminates stream loader, stops threads, and releases video capture resources."""
+ self.running = False # stop flag for Thread
+ for thread in self.threads:
+ if thread.is_alive():
+ thread.join(timeout=5) # Add timeout
+ for cap in self.caps: # Iterate through the stored VideoCapture objects
+ try:
+ cap.release() # release video capture
+ except Exception as e:
+ LOGGER.warning(f"WARNING ⚠️ Could not release VideoCapture object: {e}")
+ cv2.destroyAllWindows()
+
+ def __iter__(self):
+ """Iterates through YOLO image feed and re-opens unresponsive streams."""
+ self.count = -1
+ return self
+
+ def __next__(self):
+ """Returns the next batch of frames from multiple video streams for processing."""
+ self.count += 1
+
+ images = []
+ for i, x in enumerate(self.imgs):
+ # Wait until a frame is available in each buffer
+ while not x:
+ if not self.threads[i].is_alive() or cv2.waitKey(1) == ord("q"): # q to quit
+ self.close()
+ raise StopIteration
+ time.sleep(1 / min(self.fps))
+ x = self.imgs[i]
+ if not x:
+ LOGGER.warning(f"WARNING ⚠️ Waiting for stream {i}")
+
+ # Get and remove the first frame from imgs buffer
+ if self.buffer:
+ images.append(x.pop(0))
+
+ # Get the last frame, and clear the rest from the imgs buffer
+ else:
+ images.append(x.pop(-1) if x else np.zeros(self.shape[i], dtype=np.uint8))
+ x.clear()
+
+ return self.sources, images, [""] * self.bs
+
+ def __len__(self):
+ """Return the number of video streams in the LoadStreams object."""
+ return self.bs # 1E12 frames = 32 streams at 30 FPS for 30 years
+
+
+class LoadScreenshots:
+ """
+ Ultralytics screenshot dataloader for capturing and processing screen images.
+
+ This class manages the loading of screenshot images for processing with YOLO. It is suitable for use with
+ `yolo predict source=screen`.
+
+ Attributes:
+ source (str): The source input indicating which screen to capture.
+ screen (int): The screen number to capture.
+ left (int): The left coordinate for screen capture area.
+ top (int): The top coordinate for screen capture area.
+ width (int): The width of the screen capture area.
+ height (int): The height of the screen capture area.
+ mode (str): Set to 'stream' indicating real-time capture.
+ frame (int): Counter for captured frames.
+ sct (mss.mss): Screen capture object from `mss` library.
+ bs (int): Batch size, set to 1.
+ fps (int): Frames per second, set to 30.
+ monitor (Dict[str, int]): Monitor configuration details.
+
+ Methods:
+ __iter__: Returns an iterator object.
+ __next__: Captures the next screenshot and returns it.
+
+ Examples:
+ >>> loader = LoadScreenshots("0 100 100 640 480") # screen 0, top-left (100,100), 640x480
+ >>> for source, im, im0s, vid_cap, s in loader:
+ ... print(f"Captured frame: {im.shape}")
+ """
+
+ def __init__(self, source):
+ """Initialize screenshot capture with specified screen and region parameters."""
+ check_requirements("mss")
+ import mss # noqa
+
+ source, *params = source.split()
+ self.screen, left, top, width, height = 0, None, None, None, None # default to full screen 0
+ if len(params) == 1:
+ self.screen = int(params[0])
+ elif len(params) == 4:
+ left, top, width, height = (int(x) for x in params)
+ elif len(params) == 5:
+ self.screen, left, top, width, height = (int(x) for x in params)
+ self.mode = "stream"
+ self.frame = 0
+ self.sct = mss.mss()
+ self.bs = 1
+ self.fps = 30
+
+ # Parse monitor shape
+ monitor = self.sct.monitors[self.screen]
+ self.top = monitor["top"] if top is None else (monitor["top"] + top)
+ self.left = monitor["left"] if left is None else (monitor["left"] + left)
+ self.width = width or monitor["width"]
+ self.height = height or monitor["height"]
+ self.monitor = {"left": self.left, "top": self.top, "width": self.width, "height": self.height}
+
+ def __iter__(self):
+ """Yields the next screenshot image from the specified screen or region for processing."""
+ return self
+
+ def __next__(self):
+ """Captures and returns the next screenshot as a numpy array using the mss library."""
+ im0 = np.asarray(self.sct.grab(self.monitor))[:, :, :3] # BGRA to BGR
+ s = f"screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: "
+
+ self.frame += 1
+ return [str(self.screen)], [im0], [s] # screen, img, string
+
+
+class LoadImagesAndVideos:
+ """
+ A class for loading and processing images and videos for YOLO object detection.
+
+ This class manages the loading and pre-processing of image and video data from various sources, including
+ single image files, video files, and lists of image and video paths.
+
+ Attributes:
+ files (List[str]): List of image and video file paths.
+ nf (int): Total number of files (images and videos).
+ video_flag (List[bool]): Flags indicating whether a file is a video (True) or an image (False).
+ mode (str): Current mode, 'image' or 'video'.
+ vid_stride (int): Stride for video frame-rate.
+ bs (int): Batch size.
+ cap (cv2.VideoCapture): Video capture object for OpenCV.
+ frame (int): Frame counter for video.
+ frames (int): Total number of frames in the video.
+ count (int): Counter for iteration, initialized at 0 during __iter__().
+ ni (int): Number of images.
+
+ Methods:
+ __init__: Initialize the LoadImagesAndVideos object.
+ __iter__: Returns an iterator object for VideoStream or ImageFolder.
+ __next__: Returns the next batch of images or video frames along with their paths and metadata.
+ _new_video: Creates a new video capture object for the given path.
+ __len__: Returns the number of batches in the object.
+
+ Examples:
+ >>> loader = LoadImagesAndVideos("path/to/data", batch=32, vid_stride=1)
+ >>> for paths, imgs, info in loader:
+ ... # Process batch of images or video frames
+ ... pass
+
+ Notes:
+ - Supports various image formats including HEIC.
+ - Handles both local files and directories.
+ - Can read from a text file containing paths to images and videos.
+ """
+
+ def __init__(self, path, batch=1, vid_stride=1):
+ """Initialize dataloader for images and videos, supporting various input formats."""
+ parent = None
+ if isinstance(path, str) and Path(path).suffix == ".txt": # *.txt file with img/vid/dir on each line
+ parent = Path(path).parent
+ path = Path(path).read_text().splitlines() # list of sources
+ files = []
+ for p in sorted(path) if isinstance(path, (list, tuple)) else [path]:
+ a = str(Path(p).absolute()) # do not use .resolve() https://github.com/ultralytics/ultralytics/issues/2912
+ if "*" in a:
+ files.extend(sorted(glob.glob(a, recursive=True))) # glob
+ elif os.path.isdir(a):
+ files.extend(sorted(glob.glob(os.path.join(a, "*.*")))) # dir
+ elif os.path.isfile(a):
+ files.append(a) # files (absolute or relative to CWD)
+ elif parent and (parent / p).is_file():
+ files.append(str((parent / p).absolute())) # files (relative to *.txt file parent)
+ else:
+ raise FileNotFoundError(f"{p} does not exist")
+
+ # Define files as images or videos
+ images, videos = [], []
+ for f in files:
+ suffix = f.split(".")[-1].lower() # Get file extension without the dot and lowercase
+ if suffix in IMG_FORMATS:
+ images.append(f)
+ elif suffix in VID_FORMATS:
+ videos.append(f)
+ ni, nv = len(images), len(videos)
+
+ self.files = images + videos
+ self.nf = ni + nv # number of files
+ self.ni = ni # number of images
+ self.video_flag = [False] * ni + [True] * nv
+ self.mode = "video" if ni == 0 else "image" # default to video if no images
+ self.vid_stride = vid_stride # video frame-rate stride
+ self.bs = batch
+ if any(videos):
+ self._new_video(videos[0]) # new video
+ else:
+ self.cap = None
+ if self.nf == 0:
+ raise FileNotFoundError(f"No images or videos found in {p}. {FORMATS_HELP_MSG}")
+
+ def __iter__(self):
+ """Iterates through image/video files, yielding source paths, images, and metadata."""
+ self.count = 0
+ return self
+
+ def __next__(self):
+ """Returns the next batch of images or video frames with their paths and metadata."""
+ paths, imgs, info = [], [], []
+ while len(imgs) < self.bs:
+ if self.count >= self.nf: # end of file list
+ if imgs:
+ return paths, imgs, info # return last partial batch
+ else:
+ raise StopIteration
+
+ path = self.files[self.count]
+ if self.video_flag[self.count]:
+ self.mode = "video"
+ if not self.cap or not self.cap.isOpened():
+ self._new_video(path)
+
+ success = False
+ for _ in range(self.vid_stride):
+ success = self.cap.grab()
+ if not success:
+ break # end of video or failure
+
+ if success:
+ success, im0 = self.cap.retrieve()
+ if success:
+ self.frame += 1
+ paths.append(path)
+ imgs.append(im0)
+ info.append(f"video {self.count + 1}/{self.nf} (frame {self.frame}/{self.frames}) {path}: ")
+ if self.frame == self.frames: # end of video
+ self.count += 1
+ self.cap.release()
+ else:
+ # Move to the next file if the current video ended or failed to open
+ self.count += 1
+ if self.cap:
+ self.cap.release()
+ if self.count < self.nf:
+ self._new_video(self.files[self.count])
+ else:
+ # Handle image files (including HEIC)
+ self.mode = "image"
+ if path.split(".")[-1].lower() == "heic":
+ # Load HEIC image using Pillow with pillow-heif
+ check_requirements("pillow-heif")
+
+ from pillow_heif import register_heif_opener
+
+ register_heif_opener() # Register HEIF opener with Pillow
+ with Image.open(path) as img:
+ im0 = cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR) # convert image to BGR nparray
+ else:
+ im0 = imread(path) # BGR
+ if im0 is None:
+ LOGGER.warning(f"WARNING ⚠️ Image Read Error {path}")
+ else:
+ paths.append(path)
+ imgs.append(im0)
+ info.append(f"image {self.count + 1}/{self.nf} {path}: ")
+ self.count += 1 # move to the next file
+ if self.count >= self.ni: # end of image list
+ break
+
+ return paths, imgs, info
+
+ def _new_video(self, path):
+ """Creates a new video capture object for the given path and initializes video-related attributes."""
+ self.frame = 0
+ self.cap = cv2.VideoCapture(path)
+ self.fps = int(self.cap.get(cv2.CAP_PROP_FPS))
+ if not self.cap.isOpened():
+ raise FileNotFoundError(f"Failed to open video {path}")
+ self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride)
+
+ def __len__(self):
+ """Returns the number of files (images and videos) in the dataset."""
+ return math.ceil(self.nf / self.bs) # number of batches
+
+
+class LoadPilAndNumpy:
+ """
+ Load images from PIL and Numpy arrays for batch processing.
+
+ This class manages loading and pre-processing of image data from both PIL and Numpy formats. It performs basic
+ validation and format conversion to ensure that the images are in the required format for downstream processing.
+
+ Attributes:
+ paths (List[str]): List of image paths or autogenerated filenames.
+ im0 (List[np.ndarray]): List of images stored as Numpy arrays.
+ mode (str): Type of data being processed, set to 'image'.
+ bs (int): Batch size, equivalent to the length of `im0`.
+
+ Methods:
+ _single_check: Validate and format a single image to a Numpy array.
+
+ Examples:
+ >>> from PIL import Image
+ >>> import numpy as np
+ >>> pil_img = Image.new("RGB", (100, 100))
+ >>> np_img = np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)
+ >>> loader = LoadPilAndNumpy([pil_img, np_img])
+ >>> paths, images, _ = next(iter(loader))
+ >>> print(f"Loaded {len(images)} images")
+ Loaded 2 images
+ """
+
+ def __init__(self, im0):
+ """Initializes a loader for PIL and Numpy images, converting inputs to a standardized format."""
+ if not isinstance(im0, list):
+ im0 = [im0]
+ # use `image{i}.jpg` when Image.filename returns an empty path.
+ self.paths = [getattr(im, "filename", "") or f"image{i}.jpg" for i, im in enumerate(im0)]
+ self.im0 = [self._single_check(im) for im in im0]
+ self.mode = "image"
+ self.bs = len(self.im0)
+
+ @staticmethod
+ def _single_check(im):
+ """Validate and format an image to numpy array, ensuring RGB order and contiguous memory."""
+ assert isinstance(im, (Image.Image, np.ndarray)), f"Expected PIL/np.ndarray image type, but got {type(im)}"
+ if isinstance(im, Image.Image):
+ if im.mode != "RGB":
+ im = im.convert("RGB")
+ im = np.asarray(im)[:, :, ::-1]
+ im = np.ascontiguousarray(im) # contiguous
+ return im
+
+ def __len__(self):
+ """Returns the length of the 'im0' attribute, representing the number of loaded images."""
+ return len(self.im0)
+
+ def __next__(self):
+ """Returns the next batch of images, paths, and metadata for processing."""
+ if self.count == 1: # loop only once as it's batch inference
+ raise StopIteration
+ self.count += 1
+ return self.paths, self.im0, [""] * self.bs
+
+ def __iter__(self):
+ """Iterates through PIL/numpy images, yielding paths, raw images, and metadata for processing."""
+ self.count = 0
+ return self
+
+
+class LoadTensor:
+ """
+ A class for loading and processing tensor data for object detection tasks.
+
+ This class handles the loading and pre-processing of image data from PyTorch tensors, preparing them for
+ further processing in object detection pipelines.
+
+ Attributes:
+ im0 (torch.Tensor): The input tensor containing the image(s) with shape (B, C, H, W).
+ bs (int): Batch size, inferred from the shape of `im0`.
+ mode (str): Current processing mode, set to 'image'.
+ paths (List[str]): List of image paths or auto-generated filenames.
+
+ Methods:
+ _single_check: Validates and formats an input tensor.
+
+ Examples:
+ >>> import torch
+ >>> tensor = torch.rand(1, 3, 640, 640)
+ >>> loader = LoadTensor(tensor)
+ >>> paths, images, info = next(iter(loader))
+ >>> print(f"Processed {len(images)} images")
+ """
+
+ def __init__(self, im0) -> None:
+ """Initialize LoadTensor object for processing torch.Tensor image data."""
+ self.im0 = self._single_check(im0)
+ self.bs = self.im0.shape[0]
+ self.mode = "image"
+ self.paths = [getattr(im, "filename", f"image{i}.jpg") for i, im in enumerate(im0)]
+
+ @staticmethod
+ def _single_check(im, stride=32):
+ """Validates and formats a single image tensor, ensuring correct shape and normalization."""
+ s = (
+ f"WARNING ⚠️ torch.Tensor inputs should be BCHW i.e. shape(1, 3, 640, 640) "
+ f"divisible by stride {stride}. Input shape{tuple(im.shape)} is incompatible."
+ )
+ if len(im.shape) != 4:
+ if len(im.shape) != 3:
+ raise ValueError(s)
+ LOGGER.warning(s)
+ im = im.unsqueeze(0)
+ if im.shape[2] % stride or im.shape[3] % stride:
+ raise ValueError(s)
+ if im.max() > 1.0 + torch.finfo(im.dtype).eps: # torch.float32 eps is 1.2e-07
+ LOGGER.warning(
+ f"WARNING ⚠️ torch.Tensor inputs should be normalized 0.0-1.0 but max value is {im.max()}. "
+ f"Dividing input by 255."
+ )
+ im = im.float() / 255.0
+
+ return im
+
+ def __iter__(self):
+ """Yields an iterator object for iterating through tensor image data."""
+ self.count = 0
+ return self
+
+ def __next__(self):
+ """Yields the next batch of tensor images and metadata for processing."""
+ if self.count == 1:
+ raise StopIteration
+ self.count += 1
+ return self.paths, self.im0, [""] * self.bs
+
+ def __len__(self):
+ """Returns the batch size of the tensor input."""
+ return self.bs
+
+
+def autocast_list(source):
+ """Merges a list of sources into a list of numpy arrays or PIL images for Ultralytics prediction."""
+ files = []
+ for im in source:
+ if isinstance(im, (str, Path)): # filename or uri
+ files.append(Image.open(requests.get(im, stream=True).raw if str(im).startswith("http") else im))
+ elif isinstance(im, (Image.Image, np.ndarray)): # PIL or np Image
+ files.append(im)
+ else:
+ raise TypeError(
+ f"type {type(im).__name__} is not a supported Ultralytics prediction source type. \n"
+ f"See https://docs.ultralytics.com/modes/predict for supported source types."
+ )
+
+ return files
+
+
+def get_best_youtube_url(url, method="pytube"):
+ """
+ Retrieves the URL of the best quality MP4 video stream from a given YouTube video.
+
+ Args:
+ url (str): The URL of the YouTube video.
+ method (str): The method to use for extracting video info. Options are "pytube", "pafy", and "yt-dlp".
+ Defaults to "pytube".
+
+ Returns:
+ (str | None): The URL of the best quality MP4 video stream, or None if no suitable stream is found.
+
+ Examples:
+ >>> url = "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
+ >>> best_url = get_best_youtube_url(url)
+ >>> print(best_url)
+ https://rr4---sn-q4flrnek.googlevideo.com/videoplayback?expire=...
+
+ Notes:
+ - Requires additional libraries based on the chosen method: pytubefix, pafy, or yt-dlp.
+ - The function prioritizes streams with at least 1080p resolution when available.
+ - For the "yt-dlp" method, it looks for formats with video codec, no audio, and *.mp4 extension.
+ """
+ if method == "pytube":
+ # Switched from pytube to pytubefix to resolve https://github.com/pytube/pytube/issues/1954
+ check_requirements("pytubefix>=6.5.2")
+ from pytubefix import YouTube
+
+ streams = YouTube(url).streams.filter(file_extension="mp4", only_video=True)
+ streams = sorted(streams, key=lambda s: s.resolution, reverse=True) # sort streams by resolution
+ for stream in streams:
+ if stream.resolution and int(stream.resolution[:-1]) >= 1080: # check if resolution is at least 1080p
+ return stream.url
+
+ elif method == "pafy":
+ check_requirements(("pafy", "youtube_dl==2020.12.2"))
+ import pafy # noqa
+
+ return pafy.new(url).getbestvideo(preftype="mp4").url
+
+ elif method == "yt-dlp":
+ check_requirements("yt-dlp")
+ import yt_dlp
+
+ with yt_dlp.YoutubeDL({"quiet": True}) as ydl:
+ info_dict = ydl.extract_info(url, download=False) # extract info
+ for f in reversed(info_dict.get("formats", [])): # reversed because best is usually last
+ # Find a format with video codec, no audio, *.mp4 extension at least 1920x1080 size
+ good_size = (f.get("width") or 0) >= 1920 or (f.get("height") or 0) >= 1080
+ if good_size and f["vcodec"] != "none" and f["acodec"] == "none" and f["ext"] == "mp4":
+ return f.get("url")
+
+
+# Define constants
+LOADERS = (LoadStreams, LoadPilAndNumpy, LoadImagesAndVideos, LoadScreenshots)
diff --git a/ultralytics/data/scripts/download_weights.sh b/ultralytics/data/scripts/download_weights.sh
new file mode 100644
index 0000000000000000000000000000000000000000..f8a739f6d6199873b156da9bb24bf3c43edcff3d
--- /dev/null
+++ b/ultralytics/data/scripts/download_weights.sh
@@ -0,0 +1,18 @@
+#!/bin/bash
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Download latest models from https://github.com/ultralytics/assets/releases
+# Example usage: bash ultralytics/data/scripts/download_weights.sh
+# parent
+# └── weights
+# ├── yolov8n.pt ← downloads here
+# ├── yolov8s.pt
+# └── ...
+
+python - <