Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification (AAAI 2022)
- PyTorch >= 1.2.0
- Python3
- torchvision
- argparse
- numpy
- Imbalanced CIFAR. The original data will be downloaded and converted by imbalancec_cifar.py
- Imbalanced ImageNet
- The paper also reports results on iNaturalist 2018(https://github.com/visipedia/inat_comp).
In the code, we calculate the accuracy, which is different from that in the paper.
CIFAR-LT-100,long-tailed imabalance ratio of 200
python RISDA.py --gpu 3 --lr 0.1 --alpha 0.5 --beta 1 --imb_factor 0.005 --dataset cifar100 --num_classes 100 --save_name simple --idx cifar_im200
CIFAR-LT-100,long-tailed imabalance ratio of 100
python RISDA.py --gpu 3 --lr 0.1 --alpha 0.5 --beta 0.75 --imb_factor 0.01 --dataset cifar100 --num_classes 100 --save_name simple --idx cifar_im100
Train ResNet-50 on ImageNet-LT
CUDA_VISIBLE_DEVICES=1,0 python imagenet_ISDA_train.py /datapath/ILSVRC2012_LT/ --model resnet50 --batch-size 128 --lr 0.1 --epochs 100 --alpha_0 0 --beta_0 7.5 --workers 1 --world-size 1 --rank 0 --stage1 80 --stage2 90
Test ResNet-50 on ImageNet-LT
CUDA_VISIBLE_DEVICES=1,0 python imagenet_ISDA_train.py /datapath/ILSVRC2012_LT/ --model resnet50 --batch-size 128 --lr 0.1 --epochs 100 --alpha_0 0 --beta_0 7.5 --workers 1 --world-size 1 --rank 0 --stage1 80 --stage2 90 --evaluate checkpoint/best.pth.tar
More details will be uploaded soon.
Some codes in this project are adapted from MetaSAug and ISDA. We thank them for their excellent projects.
If you find this code useful for your research, please cite our paper.
@inproceedings{chen2021imagine,
title={Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification},
author={Chen, Xiaohua and Zhou, Yucan and Wu, Dayan and Zhang, Wanqian and Zhou, Yu and Li, Bo and Wang, Weiping},
booktitle = {Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI)},
year={2022}
}