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---
language:
- en
license: apache-2.0
tags:
- object-detection
- AgTech
- transformers
library_name: pytorch
inference: false
datasets:
- Laudando-Associates-LLC/pucks
base_model: Laudando-Associates-LLC/d-fine
base_model_relation: finetune
model-index:
- name: D-FINE Small
results:
- task:
type: object-detection
name: Object Detection
dataset:
type: Laudando-Associates-LLC/pucks
name: L&A Pucks Dataset
config: default
split: validation
metrics:
- type: mean_average_precision
name: mAP@[IoU=0.50:0.95]
value: 0.816
- type: mean_average_precision
name: [email protected]
value: 0.983
- type: mean_average_precision
name: [email protected]
value: 0.960
- type: recall
name: AR@[IoU=0.50:0.95 | maxDets=100]
value: 0.859
- type: recall
name: [email protected]
value: 0.994
- type: recall
name: [email protected]
value: 0.970
- type: f1
value: 0.951
- type: precision
value: 0.975
- type: recall
value: 0.928
- type: iou
value: 0.819
---
<h1 align="center"><strong>D-FINE Small</strong></h1>
<p align="center">
<a href="https://huggingface.co/Laudando-Associates-LLC/d-fine-small">
<img src="https://img.shields.io/badge/HuggingFace-Model-yellow?logo=huggingface&style=for-the-badge">
</a>
</p>
This repository contains the [D-FINE](https://arxiv.org/abs/2410.13842) Small model, a real-time object detector designed for efficient and accurate object detection tasks.
<p align="center">
<img src="assets/small.png" alt="Small Detections" />
</p>
## Try it in the Browser
You can test this model using our interactive Gradio demo:
<p align="center">
<a href="https://huggingface.co/spaces/Laudando-Associates-LLC/d-fine-demo">
<img src="https://img.shields.io/badge/Launch%20Demo-Gradio-FF4B4B?logo=gradio&logoColor=white&style=for-the-badge">
</a>
</p>
## Model Overview
* Architecture: D-FINE Small
* Parameters: 10.3M
* Performance:
- mAP@[0.50:0.95]: 0.816
- mAP@[0.50]: 0.983
- AR@[0.50:0.95]: 0.859
- F1 Score: 0.951
* Framework: PyTorch / ONNX
* Training Hardware: 2× NVIDIA RTX A6000 GPUs
## Download
| Format | Link |
|:--------:|:------:|
| ONNX | <a href="https://huggingface.co/Laudando-Associates-LLC/d-fine-small/resolve/main/model.onnx"><img src="https://img.shields.io/badge/-ONNX-005CED?style=for-the-badge&logo=onnx&logoColor=white"></a> |
| PyTorch | <a href="https://huggingface.co/Laudando-Associates-LLC/d-fine-small/resolve/main/pytorch_model.bin"><img src="https://img.shields.io/badge/PyTorch-EE4C2C?style=for-the-badge&logo=pytorch&logoColor=white"></a> |
## Usage
To utilize this model, ensure you have the shared [D-FINE processor](https://huggingface.co/Laudando-Associates-LLC/d-fine):
```python
from transformers import AutoProcessor, AutoModel
# Load processor
processor = AutoProcessor.from_pretrained("Laudando-Associates-LLC/d-fine", trust_remote_code=True)
# Load model
model = AutoModel.from_pretrained("Laudando-Associates-LLC/d-fine-small", trust_remote_code=True)
# Process image
inputs = processor(image)
# Run inference
outputs = model(**inputs, conf_threshold=0.4)
```
## Evaluation
This model was trained and evaluated on the [L&A Pucks Dataset](https://huggingface.co/datasets/Laudando-Associates-LLC/pucks).
## License
This model is licensed under the [Apache License 2.0](https://github.com/Peterande/D-FINE/blob/master/LICENSE).
## Citation
If you use `D-FINE` or its methods in your work, please cite the following BibTeX entries:
```latex
@misc{peng2024dfine,
title={D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement},
author={Yansong Peng and Hebei Li and Peixi Wu and Yueyi Zhang and Xiaoyan Sun and Feng Wu},
year={2024},
eprint={2410.13842},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
``` |