Dfine Large Obj365

🚀 D-FINE

D-FINE is a powerful real - time object detector. It redefines the bounding box regression task in DETR models to achieve outstanding localization precision. This model is suitable for various applications like autonomous driving, surveillance systems, etc.

🚀 Quick Start

import torch
import requests

from PIL import Image
from transformers import DFineForObjectDetection, AutoImageProcessor

url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = Image.open(requests.get(url, stream=True).raw)

image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine-large-obj365")
model = DFineForObjectDetection.from_pretrained("ustc-community/dfine-large-obj365")

inputs = image_processor(images=image, return_tensors="pt")

with torch.no_grad():
    outputs = model(**inputs)

results = image_processor.post_process_object_detection(outputs, target_sizes=torch.tensor([image.size[::-1]]), threshold=0.3)

for result in results:
    for score, label_id, box in zip(result["scores"], result["labels"], result["boxes"]):
        score, label = score.item(), label_id.item()
        box = [round(i, 2) for i in box.tolist()]
        print(f"{model.config.id2label[label]}: {score:.2f} {box}")

✨ Features

D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD). It can achieve outstanding localization precision by redefining the bounding box regression task in DETR models.

📚 Documentation

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-hf

This 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.

Training

D-FINE is trained on COCO and Objects365 (Lin et al. [2014]) train2017 and validated on COCO + Objects365 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.

📄 License

apache-2.0

📦 Additional Information