Dfine Xlarge Obj2coco

🚀 D-FINE

D-FINE is a powerful real-time object detector. It redefines the bounding box regression task in DETR models, achieving outstanding localization precision. This README provides an overview, performance details, usage examples, training information, and application scenarios of the D-FINE model.

✨ Features

• High Precision: Redefines the bounding box regression task in DETR models to achieve outstanding localization precision.
• Two Key Components: Comprises Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD).
• Versatile Applications: Ideal for real-time object detection in various fields such as autonomous driving, surveillance systems, robotics, and retail analytics.
• Flexible Deployment: Suitable for both edge devices and large-scale systems, ensuring high accuracy and speed in dynamic, real-world environments.

📦 Installation

No specific installation steps are provided in the original document. If you want to use the D-FINE model with the transformers library, you can install the transformers library via the following command:

pip install transformers

💻 Usage Examples

Basic Usage

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-xlarge-obj2coco")
model = DFineForObjectDetection.from_pretrained("ustc-community/dfine-xlarge-obj2coco")

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}")

📚 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. D-FINE comprises two key components: Fine-grained Distribution Refinement (FDR) and Global Optimal Localization Self-Distillation (GO-LSD).

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.

📄 License

This project is licensed under the Apache-2.0 license.