[CVPR 2021] MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition

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Deep Learning MetaSAug
Overview

MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition (CVPR 2021) arXiv

Prerequisite

  • PyTorch >= 1.2.0
  • Python3
  • torchvision
  • PIL
  • argparse
  • numpy

Evaluation

For faster evaluation, we provide several pre-trained models of MetaSAug. We can use MetaSAug_test_CE.sh & MetaSAug_test_LDAM.sh to test MetaSAug with cross-entropy loss and LDAM loss, respectively. The models are stored in checkpoints/ours.

Evaluation examples:

  • sh MetaSAug_test_CE.sh
  • sh MetaSAug_test_LDAM.sh

Training example

CIFAR-LT-100, MetaSAug with LDAM loss
python3.6 MetaSAug_LDAM_train.py --gpu 0 --lr 0.1 --lam 0.75 --imb_factor 0.05 --dataset cifar100 --num_classes 100 --save_name MetaSAug_cifar100_LDAM_imb0.05 --idx 1

Acknowledgements

Some codes in this project are adapted from Meta-class-weight. We thank them for their excellent projects.

Citation

If you find this code useful for your research, please cite our paper:

@inproceedings{li2021MetaSAug,
author = {Li, Shuang and Gong, Kaixiong and Liu, Chi Harold and Wang, Yulin and Qiao, Feng and Cheng, Xinjing},
title = {MetaSAug: Meta Semantic Augmentation for Long-Tailed Visual Recognition},
year = {2021},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition},
}
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Comments
  • Could you share training records about cifar100, i can't reproduce your results

    Could you share training records about cifar100, i can't reproduce your results

    I run many times for your work. For balance ratio = 0.01, the result i run is about 47%,not 48%. The other results are no different from LDAM actually.

    opened by summertaiyuan 1
  • How to deal with out of cuda memory?

    How to deal with out of cuda memory?

    Hello, looking for your reply. The code "self.CoVariance = torch.zeros(class_num, feature_num, feature_num).cuda()" needs too much cuda memory when the feature_num is large such as 2048. How to deal with it? I cannot put it onto a GPU.

    opened by qiuzhen8484 1
  • A question about the paper

    A question about the paper

    In the equation (5), we compute the gradient of covariance matrix by the standard cross-entropy loss/equation (3) on validation set. But it seems that the standard cross-entropy function don't need to use the covariance matrix to compute the loss, so the related gradient should be zero ?

    opened by darkpromise98 0
  • Could you share the code for the visualization of augmentation?

    Could you share the code for the visualization of augmentation?

    I found your work is impressive. Just want to know whether it is possible to share the code for visualization (Figure 3) as it is nontrivial to implement it.

    Thanks!

    opened by changliu816 0
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