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

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

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

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

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

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

./notebooks/imagenet-shift-calib.ipynb

./notebooks/cifar100-shift-calib.ipynb

@article{hong2020disentangling,
  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},
  journal={arXiv preprint arXiv:2012.00321},
  year={2020}
}