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The previous commit was meant to remove .github/workflows and removed every tracked path instead — 892 files, the whole repo. This puts back the 874 that were never in scope; the eighteen workflow files stay gone, for the reasons that commit gives. Content is byte-identical to 9d2aec9 outside .github/workflows.
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Anomalib Documentation
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 visual anomaly detection, where the goal of the algorithm is to detect and/or localize anomalies within images or videos in a dataset. Anomalib is constantly updated with new algorithms and training/inference extensions, so keep checking!
:::{dropdown} {octicon}checklist;1em Key Features
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- Simple and modular API and CLI for training, inference, benchmarking, and hyperparameter optimization.
- The largest public collection of ready-to-use deep learning anomaly detection algorithms and benchmark datasets.
- Lightning based model implementations to reduce boilerplate code and limit the implementation efforts to the bare essentials.
- The majority of models can be exported to OpenVINO Intermediate Representation (IR) for accelerated inference on Intel hardware.
- A set of inference tools for quick and easy deployment of the standard or custom anomaly detection models. :::
{octicon}package;1em Installation
Anomalib provides two ways to install the library. The first is through PyPI, and the second is through a local installation. PyPI installation is recommended if you want to use the library without making any changes to the source code. If you want to make changes to the library, then a local installation is recommended.
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:::{tab-item} PyPI
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:::{tab-item} Source
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{octicon}light-bulb Get Started
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:::{grid-item-card} {octicon}hourglass Anomalib in 15 minutes
🔗 markdown/get_started/anomalib
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Get started with anomalib in 15 minutes. :::
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{octicon}book Guides
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:::{grid-item-card} {octicon}codescan Reference Guide
🔗 markdown/guides/reference/index
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Learn more about anomalib API and CLI. :::
:::{grid-item-card} {octicon}question How-To Guide
🔗 markdown/guides/how_to/index
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Learn how to use anomalib for your anomaly detection tasks. :::
:::{grid-item-card} {octicon}code Developer Guide
🔗 markdown/guides/developer/index
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Learn how to develop and contribute to anomalib.
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:caption: Get Started
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:caption: Guides
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:caption: Announcements
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