This repository is the official code for the paper Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail Recognition (ICML 2024).
Paper Title: Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail Recognition.
Author: Zhiyong Yang, Qianqian Xu*, Zitai Wang, Sicong Li, Boyu Han, Shilong Bao, Xiaochun Cao, Qingming Huang*
- Clone the repository
git clone https://github.com/scongl/DirMixE.git
- Install the required libraries
pip install -r requirements.txt
- Please download these datasets and put them to the /data file.
- ImageNet-LT can be found at here.
- iNaturalist data should be the 2018 version from here.
- CIFAR-100 and CIFAR-10 will be downloaded automatically with the dataloader.
data
├── CIFAR-10
│ └── cifar-10-batches-py
├── CIFAR-100
│ └── cifar-100-python
├── ImageNet_LT
│ ├── test
│ ├── train
│ └── val
└── iNaturalist
├── test2018
└── train_val2018
- We provide txt files for test-agnostic long-tailed recognition for ImageNet-LT and iNaturalist 2018. CIFAR-100 and CIFAR-10 will be generated automatically with the code.
- For iNaturalist 2018, please unzip the iNaturalist_train.zip.
data_txt
├── ImageNet_LT
│ ├── ImageNet_LT_backward10.txt
│ ├── ImageNet_LT_backward25.txt
│ ├── ImageNet_LT_backward2.txt
│ ├── ImageNet_LT_backward50.txt
│ ├── ImageNet_LT_backward5.txt
│ ├── ImageNet_LT_forward10.txt
│ ├── ImageNet_LT_forward25.txt
│ ├── ImageNet_LT_forward2.txt
│ ├── ImageNet_LT_forward50.txt
│ ├── ImageNet_LT_forward5.txt
│ ├── ImageNet_LT_test.txt
│ ├── ImageNet_LT_train.txt
│ ├── ImageNet_LT_uniform.txt
│ └── ImageNet_LT_val.txt
└── iNaturalist18
├── iNaturalist18_backward2.txt
├── iNaturalist18_backward3.txt
├── iNaturalist18_forward2.txt
├── iNaturalist18_forward3.txt
├── iNaturalist18_train.txt
├── iNaturalist18_uniform.txt
└── iNaturalist18_val.txt
- To train the DirMixE model, run this command:
python train.py -c configs/train/cifar10.json
- To evaluate DirMixE under ours setting, run this command:
python test_train_cifar_dirmixe.py -c configs/test/test_cifar10.json -r checkpoint_path
- To evaluate DirMixE under sade's setting, run this command:
python test_train_cifar_sade.py -c configs/test/test_cifar10.json -r checkpoint_path
- To train the DirMixE model, run this command:
python train.py -c configs/train/cifar100.json
- To evaluate DirMixE under ours setting, run this command:
python test_train_cifar_dirmixe.py -c configs/test/test_cifar100.json -r checkpoint_path
- To evaluate DirMixE under sade's setting, run this command:
python test_train_cifar_sade.py -c configs/test/test_cifar100.json -r checkpoint_path
- To train the DirMixE model, run this command:
python train.py -c configs/train/imagenet.json
- To evaluate DirMixE under ours setting, run this command:
python test_train_cifar_dirmixe.py -c configs/test/test_imagenet.json -r checkpoint_path
- To evaluate DirMixE under sade's setting, run this command:
python test_train_cifar_sade.py -c configs/test/test_imagenet.json -r checkpoint_path
- To train the DirMixE model, run this command:
python train.py -c configs/train/inaturalist.json
- To evaluate DirMixE under ours setting, run this command:
python test_train_cifar_dirmixe.py -c configs/test/test_inaturalist.json -r checkpoint_path
- To evaluate DirMixE under sade's setting, run this command:
python test_train_cifar_sade.py -c configs/test/test_inaturalist.json -r checkpoint_path
If you find our work inspiring or use our codebase in your research, please cite our work.
@inproceedings{yang2024harnessing,
title={Harnessing Hierarchical Label Distribution Variations in Test Agnostic Long-tail Recognition},
author={Zhiyong Yang and Qianqian Xu and Zitai Wang and Sicong Li and Boyu Han and Shilong Bao and Xiaochun Cao and Qingming Huang},
booktitle={International Conference on Machine Learning},
year={2024}
}
Our codes are based on the repository SADE.