D-FINE
Overview
The D-FINE model was proposed in
D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement by
Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu
This model was contributed by
VladOS95-cyber with the help of
@qubvel-hfThis is the HF transformers implementation for D-FINE
_coco -> model trained on COCO
_obj365 -> model trained on Object365
_obj2coco -> model trained on Object365 and then finetuned on COCO
Performance
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).
How to use
1import torch
2import requests
3
4from PIL import Image
5from transformers import DFineForObjectDetection, AutoImageProcessor
6
7url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
8image = Image.open(requests.get(url, stream=True).raw)
9
10image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine-medium-coco")
11model = DFineForObjectDetection.from_pretrained("ustc-community/dfine-medium-coco")
12
13inputs = image_processor(images=image, return_tensors="pt")
14
15with torch.no_grad():
16 outputs = model(**inputs)
17
18results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3)
19
20for result in results:
21 for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
22 score, label = score.item(), label_id.item()
23 box = [round(i, 2) for i in box.tolist()]
24 print(f"{model.config.id2label[label]}: {score:.2f} {box}")
Training
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.
Applications
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.
