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--- |
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license: apache-2.0 |
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tags: |
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- object-detection |
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- vision |
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datasets: |
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- sku110k |
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widget: |
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- src: >- |
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https://github.com/Isalia20/DETR-finetune/blob/main/IMG_3507.jpg?raw=true |
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example_title: StoreExample(Not from SKU110K Dataset) |
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--- |
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# DETR (End-to-End Object Detection) model with ResNet-50 backbone trained on SKU110K Dataset with 400 num_queries |
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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. |
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### How to use |
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Here is how to use this model: |
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```python |
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from transformers import DetrImageProcessor, DetrForObjectDetection |
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import torch |
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from PIL import Image, ImageOps |
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import requests |
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url = "https://github.com/Isalia20/DETR-finetune/blob/main/IMG_3507.jpg?raw=true" |
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image = Image.open(requests.get(url, stream=True).raw) |
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image = ImageOps.exif_transpose(image) |
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# you can specify the revision tag if you don't want the timm dependency |
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processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50", revision="no_timm") |
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model = DetrForObjectDetection.from_pretrained("isalia99/detr-resnet-50-sku110k") |
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model = model.eval() |
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inputs = processor(images=image, return_tensors="pt") |
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outputs = model(**inputs) |
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# convert outputs (bounding boxes and class logits) to COCO API |
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# let's only keep detections with score > 0.8 |
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target_sizes = torch.tensor([image.size[::-1]]) |
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results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.8)[0] |
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for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): |
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box = [round(i, 2) for i in box.tolist()] |
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print( |
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f"Detected {model.config.id2label[label.item()]} with confidence " |
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f"{round(score.item(), 3)} at location {box}" |
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) |
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``` |
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This should output: |
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``` |
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Detected LABEL_1 with confidence 0.983 at location [665.49, 480.05, 708.15, 650.11] |
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Detected LABEL_1 with confidence 0.938 at location [204.99, 1405.9, 239.9, 1546.5] |
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... |
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Detected LABEL_1 with confidence 0.998 at location [772.85, 169.49, 829.67, 372.18] |
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Detected LABEL_1 with confidence 0.999 at location [828.28, 1475.16, 874.37, 1593.43] |
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``` |
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Currently, both the feature extractor and model support PyTorch. |
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## Training data |
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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. |
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## Training procedure |
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### Training |
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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. |
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## Evaluation results |
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This model achieves an mAP of **58.9** on SKU110k validation set. Result was calculated with torchmetrics MeanAveragePrecision class. |
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## Training Code |
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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. |
