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--- |
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library_name: transformers |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: object-detection |
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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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- coco |
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--- |
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## D-FINE |
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### **Overview** |
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The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://arxiv.org/abs/2410.13842) by |
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Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu |
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This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber) with the help of [@qubvel-hf](https://huggingface.co/qubvel-hf) |
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This is the HF transformers implementation for D-FINE |
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_coco -> model trained on COCO |
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_obj365 -> model trained on Object365 |
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_obj2coco -> model trained on Object365 and then finetuned on COCO |
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### **Performance** |
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D-FINE, a powerful real-time object detector that achieves outstanding localization precision by redefining the bounding box regression task in DETR models. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD). |
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### **How to use** |
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```python |
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import torch |
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import requests |
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from PIL import Image |
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from transformers import DFineForObjectDetection, AutoImageProcessor |
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url = 'http://images.cocodataset.org/val2017/000000039769.jpg' |
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image = Image.open(requests.get(url, stream=True).raw) |
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image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine-small-coco") |
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model = DFineForObjectDetection.from_pretrained("ustc-community/dfine-small-coco") |
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inputs = image_processor(images=image, return_tensors="pt") |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3) |
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for result in results: |
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for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]): |
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score, label = score.item(), label_id.item() |
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box = [round(i, 2) for i in box.tolist()] |
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print(f"{model.config.id2label[label]}: {score:.2f} {box}") |
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``` |
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### **Training** |
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D-FINE is trained on COCO (Lin et al. [2014]) train2017 and validated on COCO val2017 dataset. We report the standard AP metrics (averaged over uniformly sampled IoU thresholds ranging from 0.50 − 0.95 with a step size of 0.05), and APval5000 commonly used in real scenarios. |
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### **Applications** |
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D-FINE is ideal for real-time object detection in diverse applications such as **autonomous driving**, **surveillance systems**, **robotics**, and **retail analytics**. Its enhanced flexibility and deployment-friendly design make it suitable for both edge devices and large-scale systems + ensures high accuracy and speed in dynamic, real-world environments. |
