omoured/YOLOv10-Document-Layout-Analysis

🤗 Live Demo here: 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 69103 training images, 6480 validation images, and 4994 test images.

Results 📊

Model	mAP50	mAP50-95	Model Weights
YOLOv10-x	0.924	0.740	Download
YOLOv10-b	0.922	0.732	Download
YOLOv10-l	0.921	0.732	Download
YOLOv10-m	0.917	0.737	Download
YOLOv10-s	0.905	0.713	Download
YOLOv10-n	0.892	0.685	Download

Codes 🔥

Check out our Github repo for inference codes: 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/