imbalanced-ssl's Introduction

Imbalanced-SSL

Code Release for "Self-supervised Learning is More Robust to Dataset Imbalance"

Requirement

pytorch>=1.6.0
torchvision
numpy
tqdm

Data

The CIFAR data is available in the google drive link.

The ImageNet textlists is available in the google drive link.

Usage

Train on Imbalanced CIFAR

Exponential weight

python main_sam_exp.py --data_root data/ --arch resnet18 \
       --learning_rate 0.06 --epochs 1600 --weight_decay 5e-4 --momentum 0.9 \
       --batch_size 512 --gpu 0 \
       --exp_dir <exp_dir> \
       --rho 7.0 --phi 1.6

KDE weight

python main_sam_weight.py --data_root data/ --arch resnet18 \
       --learning_rate 0.06 --epochs 1600 --weight_decay 5e-4 --momentum 0.9 \
       --batch_size 512 --gpu 0 \
       --weight_path <weight_path> \
       --exp_dir <exp_dir> \

No weight

python main.py --data_root data/ --arch resnet18 \
       --learning_rate 0.06 --epochs 1600 --weight_decay 5e-4 --momentum 0.9 \
       --batch_size 512 --gpu 0 \
       --exp_dir <exp_dir> \

Evaluation on Balanced CIFAR

python main_lincls.py --arch resnet18 --num_cls 10 \
       --batch_size 256 --lr 30.0 --weight_decay 0.0 \
       --pretrained <pretraining_checkpoint_path> --gpu 0 <cifar_data_path>

Train on ImageNet

python pretrain_moco_sam_rare.py --dataset imagenet --data data/imagenet \
       --epochs 300 --rho 2 --phi 1.005 \
       --root_path <root_path> --dist-url tcp://localhost:10001

Evaluate on Downstream Datasets

CUDA_VISIBLE_DEVICES=0 python ft_moco.py <dataset_path> \
       -d <dataset_name> -sr 100 --lr 0.1 -i 2000 \
       --lr-decay-epochs 5 10 15 --epochs 20  \
       --log <output_path> --pretrained <model_checkpoint_path>

Citation

@inproceedings{
liu2022selfsupervised,
title={Self-supervised Learning is More Robust to Dataset Imbalance},
author={Hong Liu and Jeff Z. HaoChen and Adrien Gaidon and Tengyu Ma},
booktitle={International Conference on Learning Representations},
year={2022},
url={https://openreview.net/forum?id=4AZz9osqrar}
}

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