YNCao / P2P-Net

Official implementation of "Fine-Grained Object Classification via Self-Supervised Pose Alignment".

P2P-Net

Official implementation of "Fine-Grained Object Classification via Self-Supervised Pose Alignment". Accepted to CVPR2022.

Preparation

Benchmarks

CUB_200_2011 (CUB) - http://www.vision.caltech.edu/visipedia/CUB-200-2011.html

Stanford Cars (CAR) - https://ai.stanford.edu/~jkrause/cars/car_dataset.html

FGVC-Aircraft (AIR) - https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/

Unzip benchmarks to "../Data/" (update the variable "data_config" in train.py if necessary).

Training and evaluation

We train the model with 4 V100. The valid batch size is 16*4=64.

python train.py

Performance

Citation

@article{p2pnet2022,
      title={Fine-Grained Object Classification via Self-Supervised Pose Alignment}, 
      author={Xuhui Yang, Yaowei Wang, Ke Chen, Yong Xu, Yonghong Tian},
      journal={arXiv preprint arXiv:2203.15987},
      year={2022},
}

Acknowledgement

This work is supported by the China Postdoctoral Science Foundation (2021M691682), the National Natural Science Foundation of China (61902131, 62072188, U20B2052), the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (2017ZT07X183), and the Project of Peng Cheng Laboratory (PCL2021A07).