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Maximum Weight Entropy OpenOOD Benchmark

This repository provides the experiments conducted for the Maximum Weight Entropy method (MaxWEnt) within the OpenOOD Benchmark.

The OpenOOD Benchmark reproduces representative methods within the Generalized Out-of-Distribution Detection Framework, aiming to make a fair comparison across methods that initially developed for anomaly detection, novelty detection, open set recognition, and out-of-distribution detection.

Get Started

To setup the environment, we use conda to manage our dependencies.

Our developers use CUDA 10.1 to do experiments.

You can specify the appropriate cudatoolkit version to install on your machine in the environment.yml file, and then run the following to create the conda environment:

conda env create -f environment.yml
conda activate openood

Datasets and pretrained models are provided here. Please unzip the files if necessary.

The codebase accesses the datasets from ./data/ and pretrained models from ./networks/ by default.

Run MaxWEnt Experiments

The scripts to run the MaxWEnt experiments can be found in the ./mwe_scripts/ folder.

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Benchmarking Generalized Out-of-Distribution Detection

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  • Python 58.7%
  • Jupyter Notebook 26.7%
  • Shell 14.6%