PRIS-CV / PMG-Progressive-Multi-Granularity-Training

Code release for Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches (ECCV2020)

Progressive Multi-Granularity Training

Code release for Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches (ECCV2020)

Requirement

python 3.6

PyTorch >= 1.3.1

torchvision >= 0.4.2

Training

1. Download datatsets for FGVC (e.g. CUB-200-2011, Standford Cars, FGVC-Aircraft, etc) and organize the structure as follows:

dataset
├── train
│   ├── class_001
|   |      ├── 1.jpg
|   |      ├── 2.jpg
|   |      └── ...
│   ├── class_002
|   |      ├── 1.jpg
|   |      ├── 2.jpg
|   |      └── ...
│   └── ...
└── test
    ├── class_001
    |      ├── 1.jpg
    |      ├── 2.jpg
    |      └── ...
    ├── class_002
    |      ├── 1.jpg
    |      ├── 2.jpg
    |      └── ...
    └── ...

2. Train from scratch with train.py.

Citation

Please cite our paper if you use PMG in your work.

@InProceedings{du2020fine,
  title={Fine-Grained Visual Classification via Progressive Multi-Granularity Training of Jigsaw Patches},
  author={Du, Ruoyi and Chang, Dongliang and Bhunia, Ayan Kumar and Xie, Jiyang and Song, Yi-Zhe and Ma, Zhanyu and Guo, Jun},
  booktitle = {European Conference on Computer Vision},
  year={2020}
}

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