--- license: agpl-3.0 datasets: - ds4sd/DocLayNet language: - en metrics: - accuracy - mape - precision - recall pipeline_tag: object-detection --- 🤗 Live Demo here: [https://huggingface.co/spaces/omoured/YOLOv10-Document-Layout-Analysis](https://huggingface.co/spaces/omoured/YOLOv10-Document-Layout-Analysis) ## 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.

## 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) | ## Codes 🔥 Check out our Github repo for inference codes: [https://github.com/moured/YOLOv10-Document-Layout-Analysis](https://github.com/moured/YOLOv10-Document-Layout-Analysis) ## 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/)