dfine-nano-coco-ONNX

dfine-nano-coco (ONNX)#

This is an ONNX version of ustc-community/dfine-nano-coco. It was automatically converted and uploaded using this Hugging Face Space.

Usage with Transformers.js#

See the pipeline documentation for object-detection: https://huggingface.co/docs/transformers.js/api/pipelines#module_pipelines.ObjectDetectionPipeline

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-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).

Image: COCO.png

How to use#

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-nano-coco")
model = DFineForObjectDetection.from_pretrained("ustc-community/dfine-nano-coco")

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}")

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.