P2P-Net

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

P2P-Net 최신버젼 다운로드

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// repository documentation

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# P2P-Net
Official implementation of "[Fine-Grained Object Classification via Self-Supervised Pose Alignment](https://arxiv.org/abs/2203.15987)". 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.
```shell
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).

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