Learning on long-tailed CIFAR10 or CIFAR100 (CVPR2019) [paper]
python3 train.py --dataset CIFAR10 (or CIFAR100) --gpu 0 --ratio 0.1
python3 train.py --dataset CIFAR10 (or CIFAR100) --gpu 0 --ratio 0.01
python3 train.py --dataset CIFAR10 (or CIFAR100) --gpu 0 --ratio 0.005
python3 train.py --dataset CIFAR10 (or CIFAR100) --gpu 0 --ratio 0.002
If you want to utilize various loss functions, you can directly run the following code to train the model.
Focal loss (ICCV2017) [paper]
python3 train.py --dataset CIFAR10 --gpu 0 --ratio 0.1 --loss Focal
Class balanced loss (CVPR2019) [paper]
python3 train.py --dataset CIFAR10 --gpu 0 --ratio 0.1 --loss CBW
Generalized reweight loss (CVPR2021) [paper]
ppython3 train.py --dataset CIFAR10 --gpu 0 --ratio 0.1 --loss GR
Balanced softmax loss (NeurIPS2020) [paper]
python3 train.py --dataset CIFAR10 --gpu 0 --ratio 0.1 --loss BS
LADE loss (CVPR2021) [paper]
python3 train.py --dataset CIFAR10 --gpu 0 --ratio 0.1 --loss LADE
LDAM loss (NeurIPS2019) [paper]
python3 train.py --dataset CIFAR10 --gpu 0 --ratio 0.1 --loss LDAM --norm
Logit adjusted loss (ICLR2021) [paper]
python3 train.py --dataset CIFAR10 --gpu 0 --ratio 0.1 --loss LA
Vector scaling loss (NeurIPS2021) [paper]
ppython3 train.py --dataset CIFAR10 --gpu 0 --ratio 0.1 --loss VS
Influence-Balanced loss (ICCV2021) [paper]
python3 train.py --dataset CIFAR10 --gpu 0 --ratio 0.1 --loss IB
python3 train.py --dataset CIFAR10 --gpu 0 --ratio 0.1 --loss IBFL
ELM loss (SMC2023) [paper]
python3 train.py --dataset CIFAR10 --gpu 0 --ratio 0.1 --loss ELM --norm
python3 train.py --dataset CIFAR10 --gpu 0 --ratio 0.1 --loss FCE
python3 train.py --dataset CIFAR10 --gpu 0 --ratio 0.1 --loss LAFCE
Learning with class balancing weight [paper]
If you want to apply class balancing weight, you can directly run the following code to train the model.
python3 train.py --dataset CIFAR10 --gpu 0 --ratio 0.1 --weight_rule CBReweight
python3 train.py --dataset CIFAR10 --gpu 0 --ratio 0.1 --weight_rule IBReweight
If you want to apply the weighting scheduler (which was proposed in LDAM loss), you can directly run the following code to train the model.
Learning with weighting scheduler [paper]
python3 train.py --dataset CIFAR10 --gpu 0 --ratio 0.1 --weight_rule CBReweight --weight_scheduler DRW
If you want to improve the performance of your model, you can apply the hard augmentations to the model.
Mixup [paper]
python3 train.py --dataset CIFAR10 --gpu 0 --ratio 0.1 --augmentation Mixup
CutMix [paper]
python3 train.py --dataset CIFAR10 --gpu 0 --ratio 0.1 --augmentation CutMix