conda create -n longtail pip python=3.7 -y
source activate longtail
conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
pip install pyyaml tqdm matplotlib sklearn h5py tensorboard
Download pretrained caffe resnet152 model for Places-LT: please refer to link.
Prepare dataset: CIFAR-100, Places-LT, ImageNet-LT, iNaturalist 2018
For CIFAR-100 with imbalance ratio 0.01, using LADE:
python main.py --seed 1 --cfg config/CIFAR100_LT/lade.yaml --exp_name lade2021/cifar100_imb0.01_lade --cifar_imb_ratio 0.01 --remine_lambda 0.01 --alpha 0.1 --gpu 0
For PC Softmax:
python main.py --seed 1 --cfg config/Places_LT/ce.yaml --exp_name lade2021/places_pc_softmax --lr 0.05 --gpu 0,1,2,3
For LADE:
python main.py --seed 1 --cfg config/Places_LT/lade.yaml --exp_name lade2021/places_lade --lr 0.05 --remine_lambda 0.1 --alpha 0.005 --gpu 0,1,2,3
For LADE:
python main.py --seed 1 --cfg config/ImageNet_LT/lade.yaml --exp_name lade2021/imagenet_lade --lr 0.05 --remine_lambda 0.5 --alpha 0.05 --gpu 0,1,2,3
For LADE:
python main.py --seed 1 --cfg ./config/iNaturalist18/lade.yaml --exp_name lade2021/inat_lade --lr 0.1 --alpha 0.05 --gpu 0,1,2,3
For Imagenet (Section 4.3, 4.4):
./notebooks/imagenet-shift-calib.ipynb
For CIFAR-100 (Supplementary material):
./notebooks/cifar100-shift-calib.ipynb
The use of this software is released under BSD-3.
If you find our paper or this project helps your research, please kindly consider citing our paper in your publications.
@inproceedings{hong2021disentangling,
title={Disentangling label distribution for long-tailed visual recognition},
author={Hong, Youngkyu and Han, Seungju and Choi, Kwanghee and Seo, Seokjun and Kim, Beomsu and Chang, Buru},
booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
pages={6626--6636},
year={2021}
}