readme and preprocessor config

README.md

@@ -1,3 +1,77 @@
 ---
 license: apache-2.0
+tags:
+- object-detection
+- vision
+datasets:
+- sku110k
+widget:
+  - src: >-
+      https://github.com/Isalia20/DETR-finetune/blob/main/IMG_3507.jpg?raw=true
+    example_title: StoreExample(Not from SKU110K Dataset)
 ---
+
+# DETR (End-to-End Object Detection) model with ResNet-101-DC5 backbone trained on SKU110K Dataset with 400 num_queries
+
+DEtection TRansformer (DETR) model trained end-to-end on SKU110K object detection (8k annotated images) dataset. Main difference compared to the original model is it having **400** num_queries and it being pretrained on SKU110K dataset.
+
+### How to use
+
+Here is how to use this model:
+
+```python
+from transformers import DetrImageProcessor, DetrForObjectDetection
+import torch
+from PIL import Image, ImageOps
+import requests
+
+url = "https://github.com/Isalia20/DETR-finetune/blob/main/IMG_3507.jpg?raw=true"
+image = Image.open(requests.get(url, stream=True).raw)
+image = ImageOps.exif_transpose(image)
+
+# you can specify the revision tag if you don't want the timm dependency
+processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-101-dc5")
+model = DetrForObjectDetection.from_pretrained("isalia99/detr-resnet-101-dc5-sku110k")
+model = model.eval()
+inputs = processor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+
+# convert outputs (bounding boxes and class logits) to COCO API
+# let's only keep detections with score > 0.8
+target_sizes = torch.tensor([image.size[::-1]])
+results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.8)[0]
+
+for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        box = [round(i, 2) for i in box.tolist()]
+        print(
+                f"Detected {model.config.id2label[label.item()]} with confidence "
+                f"{round(score.item(), 3)} at location {box}"
+        )
+```
+This should output:
+```
+Detected LABEL_1 with confidence 0.983 at location [665.49, 480.05, 708.15, 650.11]
+Detected LABEL_1 with confidence 0.938 at location [204.99, 1405.9, 239.9, 1546.5]
+...
+Detected LABEL_1 with confidence 0.998 at location [772.85, 169.49, 829.67, 372.18]
+Detected LABEL_1 with confidence 0.999 at location [828.28, 1475.16, 874.37, 1593.43]
+```
+
+Currently, both the feature extractor and model support PyTorch. 
+
+## Training data
+
+The DETR model was trained on [SKU110K Dataset](https://github.com/eg4000/SKU110K_CVPR19), a dataset consisting of **8,219/588/2,936** annotated images for training/validation/test respectively.
+
+## Training procedure
+### Training
+
+The model was trained for 140 epochs on 1 RTX 4060 Ti GPU(Finetuning decoder only) with batch size of 8 and 70 epochs(finetuning the whole network) with batch size of 3 and accumulating gradients for 3 steps.
+
+## Evaluation results
+
+This model achieves an mAP of **59.8** on SKU110k validation set. Result was calculated with torchmetrics MeanAveragePrecision class.
+
+## Training Code
+
+Code is released in this repository [Repo Link](https://github.com/Isalia20/DETR-finetune/tree/main). However it's not finalized/tested well yet but the main stuff is in the code.

preprocessor_config.json

@@ -0,0 +1,18 @@
+{
+    "do_normalize": true,
+    "do_resize": true,
+    "feature_extractor_type": "DetrFeatureExtractor",
+    "format": "coco_detection",
+    "image_mean": [
+      0.485,
+      0.456,
+      0.406
+    ],
+    "image_std": [
+      0.229,
+      0.224,
+      0.225
+    ],
+    "max_size": 1333,
+    "size": 800
+  }