**A library for benchmarking, developing and deploying deep learning anomaly detection algorithms**
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[Key Features](#key-features) •
[Getting Started](#getting-started) •
[Docs](https://openvinotoolkit.github.io/anomalib) •
[License](https://github.com/openvinotoolkit/anomalib/blob/development/LICENSE)
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## Introduction
Anomalib is a deep learning library that aims to collect state-of-the-art anomaly detection algorithms for benchmarking on both public and private datasets. Anomalib provides several ready-to-use implementations of anomaly detection algorithms described in the recent literature, as well as a set of tools that facilitate the development and implementation of custom models. The library has a strong focus on image-based anomaly detection, where the goal of the algorithm is to identify anomalous images, or anomalous pixel regions within images in a dataset. Anomalib is constantly updated with new algorithms and training/inference extensions, so keep checking!
**Key features:**
- The largest public collection of ready-to-use deep learning anomaly detection algorithms and benchmark datasets.
- [**PyTorch Lightning**](https://www.pytorchlightning.ai/) based model implementations to reduce boilerplate code and limit the implementation efforts to the bare essentials.
- All models can be exported to [**OpenVINO**](https://www.intel.com/content/www/us/en/developer/tools/openvino-toolkit/overview.html) Intermediate Representation (IR) for accelerated inference on intel hardware.
- A set of [inference tools](#inference) for quick and easy deployment of the standard or custom anomaly detection models.
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## Getting Started
To get an overview of all the devices where `anomalib` as been tested thoroughly, look at the [Supported Hardware](https://openvinotoolkit.github.io/anomalib/#supported-hardware) section in the documentation.
### PyPI Install
You can get started with `anomalib` by just using pip.
```bash
pip install anomalib
```
### Local Install
It is highly recommended to use virtual environment when installing anomalib. For instance, with [anaconda](https://www.anaconda.com/products/individual), `anomalib` could be installed as,
```bash
yes | conda create -n anomalib_env python=3.8
conda activate anomalib_env
git clone https://github.com/openvinotoolkit/anomalib.git
cd anomalib
pip install -e .
```
## Training
By default [`python tools/train.py`](https://gitlab-icv.inn.intel.com/algo_rnd_team/anomaly/-/blob/development/train.py)
runs [PADIM](https://arxiv.org/abs/2011.08785) model on `leather` category from the [MVTec AD](https://www.mvtec.com/company/research/datasets/mvtec-ad) [(CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/) dataset.
```bash
python tools/train.py # Train PADIM on MVTec AD leather
```
Training a model on a specific dataset and category requires further configuration. Each model has its own configuration
file, [`config.yaml`](https://gitlab-icv.inn.intel.com/algo_rnd_team/anomaly/-/blob/development/padim/anomalib/models/padim/config.yaml)
, which contains data, model and training configurable parameters. To train a specific model on a specific dataset and
category, the config file is to be provided:
```bash
python tools/train.py --config