This is the implementation of the paper "Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning" (accepted to CVPR2021).
For more information, check out the paper on [arXiv].
- Python 3.8
- PyTorch 1.8.1 (>1.1.0)
- cuda 11.2
Download following datasets:
Automatically downloaded on torchvision.
(1) Download MiniImageNet train/test images[github], and prepare related datasets according to [TOPIC].
(2) or Download processed data from our Google Drive: [mini-imagenet.zip], (and locate the entire folder under datasets/ directory).
(1) Download CUB200 train/test images, and prepare related datasets according to [TOPIC]:
wget http://www.vision.caltech.edu/visipedia-data/CUB-200-2011/CUB_200_2011.tgz(2) or Download processed data from our Google Drive: [cub.zip], (and locate the entire folder under datasets/ directory).
Create a directory '../datasets' for the above three datasets and appropriately place each dataset to have following directory structure:
../ # parent directory
├── ./ # current (project) directory
│ ├── log/ # (dir.) running log
│ ├── pre/ # (dir.) trained models for test.
│ ├── utils/ # (dir.) implementation of paper
│ ├── README.md # intstruction for reproduction
│ ├── test.sh # bash for testing.
│ ├── train.py # code for training model
│ └── train.sh # bash for training model
└── datasets/
├── CIFAR100/ # CIFAR100 devkit
├── mini-imagenet/
│ ├── train/ # (dir.) training images (from Google Drive)
│ ├── test/ # (dir.) testing images (from Google Drive)
│ └── ..some csv files..
└── cub/ # (dir.) contains 200 object classes
├── train/ # (dir.) training images (from Google Drive)
└── test/ # (dir.) testing images (from Google Drive)
Choose apporopriate lines in train.sh file.
sh train.sh
- '--base_epochs' can be modified to control the initial accuracy ('Our' vs 'Our*' in our paper).
- Training takes approx. several hours until convergence (trained with one 2080 Ti or 3090 GPUs).
Pretrained models are available on our [Google Drive].
Choose apporopriate lines in test.sh file.
sh test.sh
The experimental results with 'test.sh 'for three datasets are shown below.
| Model | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|---|---|---|---|---|---|---|---|---|
| iCaRL | 64.10 | 53.28 | 41.69 | 34.13 | 27.93 | 25.06 | 20.41 | 15.48 | 13.73 |
| TOPIC | 64.10 | 56.03 | 47.89 | 42.99 | 38.02 | 34.60 | 31.67 | 28.35 | 25.86 |
| Ours | 63.97 | 65.86 | 61.31 | 57.6 | 53.39 | 50.93 | 48.27 | 45.36 | 43.32 |
| Model | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
|---|---|---|---|---|---|---|---|---|---|
| iCaRL | 61.31 | 46.32 | 42.94 | 37.63 | 30.49 | 24.00 | 20.89 | 18.80 | 17.21 |
| TOPIC | 61.31 | 45.58 | 43.77 | 37.19 | 32.38 | 29.67 | 26.44 | 25.18 | 21.80 |
| Ours | 61.45 | 63.80 | 59.53 | 55.53 | 52.50 | 49.60 | 46.69 | 43.79 | 41.92 |
| Model | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| iCaRL | 68.68 | 52.65 | 48.61 | 44.16 | 36.62 | 29.52 | 27.83 | 26.26 | 24.01 | 23.89 | 21.16 |
| TOPIC | 68.68 | 61.01 | 55.35 | 50.01 | 42.42 | 39.07 | 35.47 | 32.87 | 30.04 | 25.91 | 24.85 |
| Ours | 68.05 | 62.01 | 57.61 | 53.67 | 50.77 | 46.76 | 45.43 | 44.53 | 41.74 | 39.93 | 38.45 |
The presented results are slightly different from those in the paper, which are the average results of multiple tests.
If you use this code for your research, please consider citing:
@inproceedings{zhu2021self,
title={Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning},
author={Zhu, Kai and Cao, Yang and Zhai, Wei and Cheng, Jie and Zha, Zheng-Jun},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={6801--6810},
year={2021}
}