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ultralytics/yolo/configs/__init__.py

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+# Ultralytics YOLO 🚀, GPL-3.0 license
+
+from pathlib import Path
+from typing import Dict, Union
+
+from omegaconf import DictConfig, OmegaConf
+
+from ultralytics.yolo.configs.hydra_patch import check_config_mismatch
+
+
+def get_config(config: Union[str, DictConfig], overrides: Union[str, Dict] = None):
+    """
+    Load and merge configuration data from a file or dictionary.
+
+    Args:
+        config (Union[str, DictConfig]): Configuration data in the form of a file name or a DictConfig object.
+        overrides (Union[str, Dict], optional): Overrides in the form of a file name or a dictionary. Default is None.
+
+    Returns:
+        OmegaConf.Namespace: Training arguments namespace.
+    """
+    if overrides is None:
+        overrides = {}
+    if isinstance(config, (str, Path)):
+        config = OmegaConf.load(config)
+    elif isinstance(config, Dict):
+        config = OmegaConf.create(config)
+    # override
+    if isinstance(overrides, str):
+        overrides = OmegaConf.load(overrides)
+    elif isinstance(overrides, Dict):
+        overrides = OmegaConf.create(overrides)
+
+    check_config_mismatch(dict(overrides).keys(), dict(config).keys())
+
+    return OmegaConf.merge(config, overrides)

ultralytics/yolo/configs/default.yaml

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+# Ultralytics YOLO 🚀, GPL-3.0 license
+# Default training settings and hyperparameters for medium-augmentation COCO training
+
+task: "detect" # choices=['detect', 'segment', 'classify', 'init'] # init is a special case. Specify task to run.
+mode: "train" # choices=['train', 'val', 'predict'] # mode to run task in.
+
+# Train settings -------------------------------------------------------------------------------------------------------
+model: null # i.e. yolov8n.pt, yolov8n.yaml. Path to model file
+data: null # i.e. coco128.yaml. Path to data file
+epochs: 100 # number of epochs to train for
+patience: 50  # TODO: epochs to wait for no observable improvement for early stopping of training
+batch: 16 # number of images per batch
+imgsz: 640 # size of input images
+save: True # save checkpoints
+cache: False # True/ram, disk or False. Use cache for data loading
+device: null # cuda device, i.e. 0 or 0,1,2,3 or cpu. Device to run on
+workers: 8 # number of worker threads for data loading
+project: null # project name
+name: null # experiment name
+exist_ok: False # whether to overwrite existing experiment
+pretrained: False # whether to use a pretrained model
+optimizer: 'SGD' # optimizer to use, choices=['SGD', 'Adam', 'AdamW', 'RMSProp']
+verbose: False # whether to print verbose output
+seed: 0 # random seed for reproducibility
+deterministic: True # whether to enable deterministic mode
+single_cls: False # train multi-class data as single-class
+image_weights: False # use weighted image selection for training
+rect: False # support rectangular training
+cos_lr: False # use cosine learning rate scheduler
+close_mosaic: 10 # disable mosaic augmentation for final 10 epochs
+resume: False # resume training from last checkpoint
+# Segmentation
+overlap_mask: True # masks should overlap during training
+mask_ratio: 4 # mask downsample ratio
+# Classification
+dropout: 0.0  # use dropout regularization
+
+# Val/Test settings ----------------------------------------------------------------------------------------------------
+val: True # validate/test during training
+save_json: False # save results to JSON file
+save_hybrid: False # save hybrid version of labels (labels + additional predictions)
+conf: null # object confidence threshold for detection (default 0.25 predict, 0.001 val)
+iou: 0.7 # intersection over union (IoU) threshold for NMS
+max_det: 300 # maximum number of detections per image
+half: False # use half precision (FP16)
+dnn: False # use OpenCV DNN for ONNX inference
+plots: True # show plots during training
+
+# Prediction settings --------------------------------------------------------------------------------------------------
+source: null # source directory for images or videos
+show: False # show results if possible
+save_txt: False # save results as .txt file
+save_conf: False # save results with confidence scores
+save_crop: False # save cropped images with results
+hide_labels: False # hide labels
+hide_conf: False # hide confidence scores
+vid_stride: 1 # video frame-rate stride
+line_thickness: 3 # bounding box thickness (pixels)
+visualize: False # visualize results
+augment: False # apply data augmentation to images
+agnostic_nms: False # class-agnostic NMS
+retina_masks: False # use retina masks for object detection
+
+# Export settings ------------------------------------------------------------------------------------------------------
+format: torchscript # format to export to
+keras: False  # use Keras
+optimize: False  # TorchScript: optimize for mobile
+int8: False  # CoreML/TF INT8 quantization
+dynamic: False  # ONNX/TF/TensorRT: dynamic axes
+simplify: False  # ONNX: simplify model
+opset: 17  # ONNX: opset version
+workspace: 4  # TensorRT: workspace size (GB)
+nms: False  # CoreML: add NMS
+
+# Hyperparameters ------------------------------------------------------------------------------------------------------
+lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.01  # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937  # SGD momentum/Adam beta1
+weight_decay: 0.0005  # optimizer weight decay 5e-4
+warmup_epochs: 3.0  # warmup epochs (fractions ok)
+warmup_momentum: 0.8  # warmup initial momentum
+warmup_bias_lr: 0.1  # warmup initial bias lr
+box: 7.5  # box loss gain
+cls: 0.5  # cls loss gain (scale with pixels)
+dfl: 1.5  # dfl loss gain
+fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
+label_smoothing: 0.0
+nbs: 64  # nominal batch size
+hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4  # image HSV-Value augmentation (fraction)
+degrees: 0.0  # image rotation (+/- deg)
+translate: 0.1  # image translation (+/- fraction)
+scale: 0.5  # image scale (+/- gain)
+shear: 0.0  # image shear (+/- deg)
+perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0  # image flip up-down (probability)
+fliplr: 0.5  # image flip left-right (probability)
+mosaic: 1.0  # image mosaic (probability)
+mixup: 0.0  # image mixup (probability)
+copy_paste: 0.0  # segment copy-paste (probability)
+
+# Hydra configs --------------------------------------------------------------------------------------------------------
+hydra:
+  output_subdir: null  # disable hydra directory creation
+  run:
+    dir: .
+
+# Debug, do not modify -------------------------------------------------------------------------------------------------
+v5loader: False  # use legacy YOLOv5 dataloader

ultralytics/yolo/configs/hydra_patch.py

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+# Ultralytics YOLO 🚀, GPL-3.0 license
+
+import sys
+from difflib import get_close_matches
+from textwrap import dedent
+
+import hydra
+from hydra.errors import ConfigCompositionException
+from omegaconf import OmegaConf, open_dict  # noqa
+from omegaconf.errors import ConfigAttributeError, ConfigKeyError, OmegaConfBaseException  # noqa
+
+from ultralytics.yolo.utils import LOGGER, colorstr
+
+
+def override_config(overrides, cfg):
+    override_keys = [override.key_or_group for override in overrides]
+    check_config_mismatch(override_keys, cfg.keys())
+    for override in overrides:
+        if override.package is not None:
+            raise ConfigCompositionException(f"Override {override.input_line} looks like a config group"
+                                             f" override, but config group '{override.key_or_group}' does not exist.")
+
+        key = override.key_or_group
+        value = override.value()
+        try:
+            if override.is_delete():
+                config_val = OmegaConf.select(cfg, key, throw_on_missing=False)
+                if config_val is None:
+                    raise ConfigCompositionException(f"Could not delete from config. '{override.key_or_group}'"
+                                                     " does not exist.")
+                elif value is not None and value != config_val:
+                    raise ConfigCompositionException("Could not delete from config. The value of"
+                                                     f" '{override.key_or_group}' is {config_val} and not"
+                                                     f" {value}.")
+
+                last_dot = key.rfind(".")
+                with open_dict(cfg):
+                    if last_dot == -1:
+                        del cfg[key]
+                    else:
+                        node = OmegaConf.select(cfg, key[:last_dot])
+                        del node[key[last_dot + 1:]]
+
+            elif override.is_add():
+                if OmegaConf.select(cfg, key, throw_on_missing=False) is None or isinstance(value, (dict, list)):
+                    OmegaConf.update(cfg, key, value, merge=True, force_add=True)
+                else:
+                    assert override.input_line is not None
+                    raise ConfigCompositionException(
+                        dedent(f"""\
+                    Could not append to config. An item is already at '{override.key_or_group}'.
+                    Either remove + prefix: '{override.input_line[1:]}'
+                    Or add a second + to add or override '{override.key_or_group}': '+{override.input_line}'
+                    """))
+            elif override.is_force_add():
+                OmegaConf.update(cfg, key, value, merge=True, force_add=True)
+            else:
+                try:
+                    OmegaConf.update(cfg, key, value, merge=True)
+                except (ConfigAttributeError, ConfigKeyError) as ex:
+                    raise ConfigCompositionException(f"Could not override '{override.key_or_group}'."
+                                                     f"\nTo append to your config use +{override.input_line}") from ex
+        except OmegaConfBaseException as ex:
+            raise ConfigCompositionException(f"Error merging override {override.input_line}").with_traceback(
+                sys.exc_info()[2]) from ex
+
+
+def check_config_mismatch(overrides, cfg):
+    mismatched = [option for option in overrides if option not in cfg and 'hydra.' not in option]
+
+    for option in mismatched:
+        LOGGER.info(f"{colorstr(option)} is not a valid key. Similar keys: {get_close_matches(option, cfg, 3, 0.6)}")
+    if mismatched:
+        exit()
+
+
+hydra._internal.config_loader_impl.ConfigLoaderImpl._apply_overrides_to_config = override_config