Awesome
PedesFormer
PedesFormer is a MMDetection and SwinTransformer based repository. It is a successor to our earlier work Pedestron. PedesFormer, focuses on the adavancement of reseach on pedestrian detection using transformer networks.
<img title="Amsterdam" src="gifs/1.gif" width="400" /> <img title="Amsterdam" src="gifs/2.gif" width="400"/> <img title="Amsterdam" src="gifs/3.gif" width="400"/> <img title="Amsterdam" src="gifs/4.gif" width="400"/>
:fire: Updates :fire:
- 🧨 Swin Transformer CityPerson model released. 🧨
Pretrained Models
Benchmarking
Benchmarking of pre-trained models on pedestrian detection datasets (autonomous driving)
Backbone | Dataset | Backbone | Configuration | Reasonable | Heavy |
---|---|---|---|---|---|
[Cascade Mask R-CNN] | CityPersons | Swin - Transformer | Config | 9.2 | 36.9 |
[Cascade Mask R-CNN] | EuroCity Persons | Swin - Transformer | -- | ||
[Cascade Mask R-CNN] | Crowd Human | Swin - Transformer | -- |
More Pre-trained models are coming soon.
Installation
For installation, please see this.
Citation
Please cite the following works
@InProceedings{Hasan_2021_CVPR,
author = {Hasan, Irtiza and Liao, Shengcai and Li, Jinpeng and Akram, Saad Ullah and Shao, Ling},
title = {Generalizable Pedestrian Detection: The Elephant in the Room},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2021},
pages = {11328-11337}
}
@article{hasan2022pedestrian,
title={Pedestrian Detection: Domain Generalization, CNNs, Transformers and Beyond},
author={Hasan, Irtiza and Liao, Shengcai and Li, Jinpeng and Akram, Saad Ullah and Shao, Ling},
journal={arXiv preprint arXiv:2201.03176},
year={2022}
}