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YOLOv10-Document-Layout-Analysis

Object Detection
English
Model card Files
YOLOv10-Document-Layout-Analysis
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<!-- ABOUT THE PROJECT -->
## About 📋

The models were fine-tuned using 4xA100 GPUs on the Doclaynet-base dataset, which consists of 6910 training images, 648 validation images, and 499 test images.

<p align="center">
  <img src="https://github.com/moured/YOLOv10-Document-Layout-Analysis/raw/main/images/samples.gif" height="320"/>
</p>

## Results 📊
| Model   | mAP50 | mAP50-95 | Model Weights |
|---------|-------|----------|---------------|
| YOLOv10-x | 0.924 | 0.740 | [Download](https://github.com/moured/YOLOv10-Document-Layout-Analysis/releases/download/doclaynet_weights/yolov10x_best.pt) |
| YOLOv10-b | 0.922 | 0.732 | [Download](https://github.com/moured/YOLOv10-Document-Layout-Analysis/releases/download/doclaynet_weights/yolov10b_best.pt) |
| YOLOv10-l | 0.921 | 0.732 | [Download](https://github.com/moured/YOLOv10-Document-Layout-Analysis/releases/download/doclaynet_weights/yolov10l_best.pt) | 
| YOLOv10-m | 0.917 | 0.737 | [Download](https://github.com/moured/YOLOv10-Document-Layout-Analysis/releases/download/doclaynet_weights/yolov10m_best.pt) | 
| YOLOv10-s | 0.905 | 0.713 | [Download](https://github.com/moured/YOLOv10-Document-Layout-Analysis/releases/download/doclaynet_weights/yolov10s_best.pt) | 
| YOLOv10-n | 0.892 | 0.685 | [Download](https://github.com/moured/YOLOv10-Document-Layout-Analysis/releases/download/doclaynet_weights/yolov10n_best.pt) |

### Installation 💻
```
conda create -n yolov10 python=3.9
conda activate yolov10
git clone https://github.com/THU-MIG/yolov10.git
cd yolov10
pip install -r requirements.txt
pip install -e .
```

## References 📝

1. YOLOv10
```
BibTeX
@article{wang2024yolov10,
  title={YOLOv10: Real-Time End-to-End Object Detection},
  author={Wang, Ao and Chen, Hui and Liu, Lihao and Chen, Kai and Lin, Zijia and Han, Jungong and Ding, Guiguang},
  journal={arXiv preprint arXiv:2405.14458},
  year={2024}
}
```

   
2. DocLayNet
```
@article{doclaynet2022,
  title = {DocLayNet: A Large Human-Annotated Dataset for Document-Layout Analysis},  
  doi = {10.1145/3534678.353904},
  url = {https://arxiv.org/abs/2206.01062},
  author = {Pfitzmann, Birgit and Auer, Christoph and Dolfi, Michele and Nassar, Ahmed S and Staar, Peter W J},
  year = {2022}
}
```

## Contact
LinkedIn: [https://www.linkedin.com/in/omar-moured/](https://www.linkedin.com/in/omar-moured/)