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This repository contains the code and resources for zero-shot anomaly detection question, focusing on unsupervised anomaly detection in both industrial and medical domains.

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ZeroShot Anomaly Detection (ZSAD) Evaluation Environment

Overview

This repository contains the code and resources for zero-shot anomaly detection question, focusing on unsupervised anomaly detection in both industrial and medical domains.

Contents

  • datasets: Contains the rayan_dataset.py file for loading and managing dataset classes.
  • evaluation: Directory containing evaluation scripts and metrics calculation files.
  • utils: Utility functions for handling and saving output scores.
  • docker-compose.yml: A sample Configuration file for Docker setup.
  • Dockerfile: A sample Docker configuration file to replicate the test environment.
  • requirements.txt: Your python dependencies.
  • run.sh: Your script to execute your code and generate output scores.

Usage

Notice: You must place your files here and also fill in the requirements.txt and run.sh files. Setup the Environment: Use the provided Docker configuration to set up a test environment identical to the test environment and evaluate your code.

   docker compose up

For more information, you can follow the instructions provided in the RAYAN AI competition panel.

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This repository contains the code and resources for zero-shot anomaly detection question, focusing on unsupervised anomaly detection in both industrial and medical domains.

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  • Python 98.2%
  • Dockerfile 1.6%
  • Shell 0.2%