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Detecting Out-of-distribution Data through In-distribution Class Prior

This is the source code for our paper: Detecting Out-of-distribution Data through In-distribution Class Prior by Xue Jiang, Feng Liu, Zhen Fang, Hong Chen, Tongliang Liu, Feng Zheng, and Bo Han. Code is modified from GradNorm.

Usage

1. Install

conda create -n class_prior python=3.8
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
pip install scikit-learn

2. Dataset Preparation

In-distribution dataset

Please download ImageNet-1k and place the training data (not necessary) and validation data in ./dataset/id_data/imagenet_train and ./dataset/id_data/imagenet_val, respectively.

The meta file for ImageNet-LT-a8 is in ./meta.

Out-of-distribution dataset

Following MOS, we use the following 4 OOD datasets for evaluation: iNaturalist, SUN, Places, and Textures.

Please refer to MOS, download OOD datasets and put them into ./dataset/ood_data/.

3. Pre-trained Model Preparation

We use mmclassification to train ResNet101 on ImageNet-LT-a8 dataset.

Put the downloaded model in ./checkpoints/.

4. OOD Detection Evaluation

To reproduce our results, please run:

bash ./run.sh

Citation

If you find our codebase useful, please cite our work:

@inproceedings{jiang2023detecting,
        title={Detecting Out-of-distribution Data through In-distribution Class Prior}, 
        author={Xue Jiang and Feng Liu and Zhen Fang and Hong Chen and Tongliang Liu and Feng Zheng and Bo Han},
        booktitle = {ICML},
        year = {2023}
}