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*This model was published in HF papers on 2024-10-17 and contributed to Hugging Face Transformers on 2025-04-29.*

# D-FINE

## Overview

The D-FINE model was proposed in [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://huggingface.co/papers/2410.13842) by
Yansong Peng, Hebei Li, Peixi Wu, Yueyi Zhang, Xiaoyan Sun, Feng Wu

The abstract from the paper is the following:

*We introduce 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).
FDR transforms the regression process from predicting fixed coordinates to iteratively refining probability distributions, providing a fine-grained intermediate representation that significantly enhances localization accuracy. GO-LSD is a bidirectional optimization strategy that transfers localization knowledge from refined distributions to shallower layers through self-distillation, while also simplifying the residual prediction tasks for deeper layers. Additionally, D-FINE incorporates lightweight optimizations in computationally intensive modules and operations, achieving a better balance between speed and accuracy. Specifically, D-FINE-L / X achieves 54.0% / 55.8% AP on the COCO dataset at 124 / 78 FPS on an NVIDIA T4 GPU. When pretrained on Objects365, D-FINE-L / X attains 57.1% / 59.3% AP, surpassing all existing real-time detectors. Furthermore, our method significantly enhances the performance of a wide range of DETR models by up to 5.3% AP with negligible extra parameters and training costs. Our code and pretrained models: this https URL.*

This model was contributed by [VladOS95-cyber](https://github.com/VladOS95-cyber).
The original code can be found [here](https://github.com/Peterande/D-FINE).

## Usage tips

```python
import torch

from transformers import AutoImageProcessor, DFineForObjectDetection
from transformers.image_utils import load_image


url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
image = load_image(url)

image_processor = AutoImageProcessor.from_pretrained("ustc-community/dfine_x_coco")
model = DFineForObjectDetection.from_pretrained("ustc-community/dfine_x_coco", device_map="auto")

inputs = image_processor(images=image, return_tensors="pt").to(model.device)

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

results = image_processor.post_process_object_detection(outputs, target_sizes=[(image.height, image.width)], threshold=0.5)

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}")
cat: 0.96 [344.49, 23.4, 639.84, 374.27]
cat: 0.96 [11.71, 53.52, 316.64, 472.33]
remote: 0.95 [40.46, 73.7, 175.62, 117.57]
sofa: 0.92 [0.59, 1.88, 640.25, 474.74]
remote: 0.89 [333.48, 77.04, 370.77, 187.3]
```

## DFineConfig

[[autodoc]] DFineConfig

## DFineModel

[[autodoc]] DFineModel
    - forward

## DFineForObjectDetection

[[autodoc]] DFineForObjectDetection
    - forward
