metadata
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
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 📝
- 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}
}
- 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/