Official implementation of Influence-balanced Loss for Imbalanced Visual Classification in PyTorch.

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Deep LearningIB-Loss
Overview

Influence-balanced Loss for Imbalanced Visual Classification (ICCV, 2021)

This is the official implementation of Influence-balanced Loss for Imbalanced Visual Classification in PyTorch. The code heavily relies on LDAM-DRW.

Requirements

All codes are written by Python 3.7, and 'requirements.txt' contains required Python packages. To install requirements:

pip install -r requirements.txt

Dataset

Create 'data/' directory and download original data in the directory to make imbalanced versions.

  • Imbalanced CIFAR. The original data will be downloaded and converted by imbalancec_cifar.py.
  • Imbalanced Tiny ImageNet. Download the data first, and convert them by imbalance_tinyimagenet.py.
  • The paper also reports results on iNaturalist 2018. We will update the code for iNaturalist 2018 later.

Training

We provide several training examples:

CIFAR

  • CE baseline (CIFAR-100, long-tailed imabalance ratio of 100)
python cifar_train.py --dataset cifar100 --loss_type CE --train_rule None --imb_type exp --imb_factor 0.01 --epochs 200 --num_classes 100 --gpu 0
  • IB (CIFAR-100, long-tailed imabalance ratio of 100)
python cifar_train.py --dataset cifar100 --loss_type IB --train_rule IBReweight --imb_type exp --imb_factor 0.01 --epochs 200 --num_classes 100 --start_ib_epoch 100 --gpu 0
  • IB + CB (CIFAR-100, long-tailed imabalance ratio of 100)
python cifar_train.py --dataset cifar100 --loss_type IB --train_rule CBReweight --imb_type exp --imb_factor 0.01 --epochs 200 --num_classes 100 --start_ib_epoch 100 --gpu 0
  • IB + Focal (CIFAR-100, long-tailed imabalance ratio of 100)
python cifar_train.py --dataset cifar100 --loss_type IBFocal --train_rule IBReweight --imb_type exp --imb_factor 0.01 --epochs 200 --num_classes 100 --start_ib_epoch 100 --gpu 0

Tiny ImageNet

  • CE baseline (long-tailed imabalance ratio of 100)
python tinyimage_train.py --dataset tinyimagenet -a resnet18 --loss_type CE --train_rule None --imb_type exp --imb_factor 0.01 --epochs 100 --lr 0.1  --num_classes 200
  • IB (long-tailed imabalance ratio of 100)
python tinyimage_train.py --dataset tinyimagenet -a resnet18 --loss_type IB --train_rule IBReweight --imb_type exp --imb_factor 0.01 --epochs 100 --lr 0.1  --num_classes 200 --start_ib_epoch 50

Citation

If you find our paper and repo useful, please cite our paper

Owner
Seulki Park
PhD Student in Electrical and Computer Engineering at Seoul National University, Korea
Seulki Park
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