Detr Resnet 50 Sku110k

🚀 DETR (End-to-End Object Detection) model with ResNet-50 backbone trained on SKU110K Dataset with 400 num_queries

An end-to-end DETR model trained on the SKU110K object detection dataset, featuring 400 num_queries and pre-training on SKU110K.

🚀 Quick Start

💻 Usage Examples

Basic Usage

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-50", revision="no_timm")
model = DetrForObjectDetection.from_pretrained("isalia99/detr-resnet-50-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.

📚 Documentation

📦 Training Data

The DETR model was trained on SKU110K Dataset, 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 58.9 on SKU110k validation set. Result was calculated with torchmetrics MeanAveragePrecision class.

📄 Training Code

Code is released in this repository Repo Link. However it's not finalized/tested well yet but the main stuff is in the code.

📄 License

This project is licensed under the Apache-2.0 license.