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Balanced Energy Regularization Loss for Out-of-distribution Detection

This repo contains the official implementation for the CVPR2023 paper:

Balanced Energy Regularization Loss for Out-of-distribution Detection

by Hyunjun Choi, Hawook Jeong, and Jin Young Choi.

Arxiv

Our code heavily relies on the implementation of Energy-based Out-of-distribution Detection and PEBAL

Prerequisite

Prepare Dataset

Segmentation

We follow the installation process of PEBAL

All data tree has to be inserted in path: Balanced_Energy/segmentation/code/dataset

Classification

We use cifar10, cifar100 as training data

We use auxiliary data as 300K random images following Outlier Exposure

We test on the SC-OOD benchmark ,this should be inserted in data tree which can be downloaded from SC-OOD UDG

classification/data
├── cifar10
├── cifar100
├── data 
│       ├── images
│       └── imglist
└── tinyimages80m
    └── 300K_random_images.npy

Install dependencies

The project is based on the pytorch 1.8.1 with python 3.8.

1. create conda env

   $ conda env create -f balanced.yml
   

2. install the torch 1.8.1

   $ conda activate balanced
   # IF cuda version < 11.0
   $ pip install torch==1.8.1+cu102 torchvision==0.9.1+cu102 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
   # IF cuda version >= 11.0 (e.g., 30x or above)
   $ pip install torch==1.8.1+cu111 torchvision==0.9.1+cu111 torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
   

Prepare checkpoint

All checkpoint is in the zip files following google drive link:

checkpoint

Segmentation

put the checkpoint in the path : Balanced_Energy/segmentation/ckpts/pebal_balanced/

additionally, to finetune from the nvidia cityscapes model,

we follow Meta-OoD and use the deeplabv3+ checkpoint in here. you'll need to put it in "ckpts/pretrained_ckpts" directory, and please note that downloading the checkpoint before running the code is necessary

Classification

put the checkpoint in the path : Balanced_Energy/classification/snapshots/pretrained

Segmentation code run

Data path Setting in Config File

Open Balanced_Energy/segmentation/code/config/config.py

Set the root path for datasets

Use package run the training : main.py

1. python code/main.py in the Balanced_Energy/segmentation/

Use package run the evaluation : test.py

2. python code/test.py in the Balanced_Energy/segmentation/

Classification code run

in the Balanced_Energy/classification/

run ResNet18 balanced_energy_fine_tune training and testing for cifar10 with trial index 3

bash inf_run_res.sh energy_ft 0 3 

run ResNet18 balanced_energy_fine_tune training and testing for cifar100 with trial index 3

bash inf_run_res.sh energy_ft 1 3

run ResNet18 balanced_energy_fine_tune training and testing for cifar10-LT with trial index 3

bash inf_run_im_res.sh energy_ft 0 3 

run ResNet18 balanced_energy_fine_tune training and testing for cifar100-LT with trial index 3

bash inf_run_im_res.sh energy_ft 1 3

the setting of hyperparameter alpha and gamma can be controlled in the bash script

Citation

If you find this project useful, please consider the citation:

@inproceedings{choi2023balanced,
  title={Balanced Energy Regularization Loss for Out-of-Distribution Detection},
  author={Choi, Hyunjun and Jeong, Hawook and Choi, Jin Young},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={15691--15700},
  year={2023}
}