anomalib

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

https://github.com/open-edge-platform/anomalib

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Keywords

anomaly-detection anomaly-localization anomaly-segmentation geti neural-network-compression openvino unsupervised-learning

Keywords from Contributors

application mesh medical-imaging interactive

Last synced: 6 months ago · JSON representation ·

Repository

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

Basic Info

Host: GitHub
Owner: open-edge-platform
License: apache-2.0
Language: Python
Default Branch: main
Homepage: https://anomalib.readthedocs.io/en/latest/
Size: 61.5 MB

Statistics

Stars: 4,895
Watchers: 48
Forks: 799
Open Issues: 109
Releases: 40

Topics

anomaly-detection anomaly-localization anomaly-segmentation geti neural-network-compression openvino unsupervised-learning

Created over 4 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog Contributing License Code of conduct Citation Codeowners Security Governance

**A library for benchmarking, developing and deploying deep learning anomaly detection algorithms** --- [Key Features](#key-features) • [Docs](https://anomalib.readthedocs.io/en/latest/) • [Notebooks](examples/notebooks) • [License](LICENSE) [![python](https://img.shields.io/badge/python-3.10%2B-green)]() [![pytorch](https://img.shields.io/badge/pytorch-2.0%2B-orange)]() [![lightning](https://img.shields.io/badge/lightning-2.2%2B-blue)]() [![openvino](https://img.shields.io/badge/openvino-2024.0%2B-purple)]() [![Pre-Merge Checks](https://github.com/open-edge-platform/anomalib/actions/workflows/pre_merge.yml/badge.svg)](https://github.com/open-edge-platform/anomalib/actions/workflows/pre_merge.yml) [![codecov](https://codecov.io/gh/open-edge-platform/anomalib/branch/main/graph/badge.svg?token=Z6A07N1BZK)](https://codecov.io/gh/open-edge-platform/anomalib) [![Downloads](https://static.pepy.tech/personalized-badge/anomalib?period=total&units=international_system&left_color=grey&right_color=green&left_text=PyPI%20Downloads)](https://pepy.tech/project/anomalib) [![snyk](https://snyk.io/advisor/python/anomalib/badge.svg)](https://snyk.io/advisor/python/anomalib) [![OpenSSF Best Practices](https://www.bestpractices.dev/projects/8330/badge)](https://www.bestpractices.dev/projects/8330) [![ReadTheDocs](https://readthedocs.org/projects/anomalib/badge/?version=latest)](https://anomalib.readthedocs.io/en/latest/?badge=latest) [![Anomalib - Gurubase docs](https://img.shields.io/badge/Gurubase-Ask%20Anomalib%20Guru-006BFF)](https://gurubase.io/g/anomalib)

🌟 Announcing v2.1.0 Release! 🌟

We're excited to announce the release of Anomalib v2.1.0! This version brings several state-of-the-art models and anomaly detection datasets. Key features include:

New models :

🖼️ UniNet (CVPR 2025): A contrastive learning-guided unified framework with feature selection for anomaly detection.

🖼️ Dinomaly (CVPR 2025): A 'less is more philosophy' encoder-decoder architecture model leveraging pre-trained foundational models.

🎥 Fuvas (ICASSP 2025): Few-shot unsupervised video anomaly segmentation via low-rank factorization of spatio-temporal features.

New datasets:

MVTec AD 2 : A new version of the MVTec AD dataset with 8 categories of industrial anomaly detection.

MVTec LOCO AD : MVTec logical constraints anomaly detection dataset that includes both structural and logical anomalies.

Real-IAD : A real-world multi-view dataset for benchmarking versatile industrial anomaly detection.

VAD dataset : Valeo Anomaly Dataset (VAD) showcasing a diverse range of defects, from highly obvious to extremely subtle.

We value your input! Please share feedback via GitHub Issues or our Discussions

👋 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 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!

A prediction made by anomalib

Key features

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.

📦 Installation

Anomalib can be installed from PyPI. We recommend using a virtual environment and a modern package installer like uv or pip.

🚀 Quick Install

For a standard installation, you can use uv or pip. This will install the latest version of Anomalib with its core dependencies. PyTorch will be installed based on its default behavior, which usually works for CPU and standard CUDA setups.

```bash

With uv

uv pip install anomalib

Or with pip

pip install anomalib ```

For more control over the installation, such as specifying the PyTorch backend (e.g., XPU, CUDA and ROCm) or installing extra dependencies for specific models, see the advanced options below.

💡 Advanced Installation: Specify Hardware Backend

To ensure compatibility with your hardware, you can specify a backend during installation. This is the recommended approach for production environments and for hardware other than CPU or standard CUDA. **Using `uv`:** ```bash # CPU support (default, works on all platforms) uv pip install "anomalib[cpu]" # CUDA 12.4 support (Linux/Windows with NVIDIA GPU) uv pip install "anomalib[cu124]" # CUDA 12.1 support (Linux/Windows with NVIDIA GPU) uv pip install "anomalib[cu121]" # CUDA 11.8 support (Linux/Windows with NVIDIA GPU) uv pip install "anomalib[cu118]" # ROCm support (Linux with AMD GPU) uv pip install "anomalib[rocm]" # Intel XPU support (Linux with Intel GPU) uv pip install "anomalib[xpu]" ``` **Using `pip`:** The same extras can be used with `pip`: ```bash pip install "anomalib[cu124]" ```

🧩 Advanced Installation: Additional Dependencies

🔧 Advanced Installation: Install from Source

For contributing to `anomalib` or using a development version, you can install from source. **Using `uv`:** This is the recommended method for developers as it uses the project's lock file for reproducible environments. ```bash git clone https://github.com/open-edge-platform/anomalib.git cd anomalib # Create the virtual environment uv venv # Sync with the lockfile for a specific backend (e.g., CPU) uv sync --extra cpu # Or for a different backend like CUDA 12.4 uv sync --extra cu124 # To set up a full development environment uv sync --extra dev --extra cpu ``` **Using `pip`:** ```bash git clone https://github.com/open-edge-platform/anomalib.git cd anomalib # Install in editable mode with a specific backend pip install -e ".[cpu]" # Install with development dependencies pip install -e ".[dev,cpu]" ```

🧠 Training

Anomalib supports both API and CLI-based training approaches:

🔌 Python API

```python from anomalib.data import MVTecAD from anomalib.models import Patchcore from anomalib.engine import Engine

Initialize components

datamodule = MVTecAD() model = Patchcore() engine = Engine()

Train the model

engine.fit(datamodule=datamodule, model=model) ```

⌨️ Command Line

```bash

Train with default settings

anomalib train --model Patchcore --data anomalib.data.MVTecAD

Train with custom category

anomalib train --model Patchcore --data anomalib.data.MVTecAD --data.category transistor

Train with config file

anomalib train --config path/to/config.yaml ```

🤖 Inference

Anomalib provides multiple inference options including Torch, Lightning, Gradio, and OpenVINO. Here's how to get started:

🔌 Python API

```python

Load model and make predictions

predictions = engine.predict( datamodule=datamodule, model=model, ckpt_path="path/to/checkpoint.ckpt", ) ```

⌨️ Command Line

```bash

Basic prediction

anomalib predict --model anomalib.models.Patchcore \ --data anomalib.data.MVTecAD \ --ckpt_path path/to/model.ckpt

Prediction with results

anomalib predict --model anomalib.models.Patchcore \ --data anomalib.data.MVTecAD \ --ckptpath path/to/model.ckpt \ --returnpredictions ```

📘 Note: For advanced inference options including Gradio and OpenVINO, check our Inference Documentation.

Training on Intel GPUs

[!Note] Currently, only single GPU training is supported on Intel GPUs. These commands were tested on Arc 750 and Arc 770.

Ensure that you have PyTorch with XPU support installed. For more information, please refer to the PyTorch XPU documentation

🔌 API

```python from anomalib.data import MVTecAD from anomalib.engine import Engine, SingleXPUStrategy, XPUAccelerator from anomalib.models import Stfpm

engine = Engine( strategy=SingleXPUStrategy(), accelerator=XPUAccelerator(), ) engine.train(Stfpm(), datamodule=MVTecAD()) ```

⌨️ CLI

bash anomalib train --model Padim --data MVTecAD --trainer.accelerator xpu --trainer.strategy xpu_single

⚙️ Hyperparameter Optimization

Anomalib supports hyperparameter optimization (HPO) using Weights & Biases and Comet.ml.

```bash

Run HPO with Weights & Biases

anomalib hpo --backend WANDB --sweep_config tools/hpo/configs/wandb.yaml ```

📘 Note: For detailed HPO configuration, check our HPO Documentation.

🧪 Experiment Management

Track your experiments with popular logging platforms through PyTorch Lightning loggers:

📊 Weights & Biases
📈 Comet.ml
📉 TensorBoard

Enable logging in your config file to track:

Hyperparameters
Metrics
Model graphs
Test predictions

📘 Note: For logging setup, see our Logging Documentation.

📊 Benchmarking

Evaluate and compare model performance across different datasets:

```bash

Run benchmarking with default configuration

anomalib benchmark --config tools/experimental/benchmarking/sample.yaml ```

💡 Tip: Check individual model performance in their respective README files:

Patchcore Results

Other Models

✍️ Reference

If you find Anomalib useful in your research or work, please cite:

tex @inproceedings{akcay2022anomalib, title={Anomalib: A deep learning library for anomaly detection}, author={Akcay, Samet and Ameln, Dick and Vaidya, Ashwin and Lakshmanan, Barath and Ahuja, Nilesh and Genc, Utku}, booktitle={2022 IEEE International Conference on Image Processing (ICIP)}, pages={1706--1710}, year={2022}, organization={IEEE} }

👥 Contributing

We welcome contributions! Check out our Contributing Guide to get started.

Thank you to all our contributors!

Owner

Name: Open Edge Platform
Login: open-edge-platform
Kind: organization
Email: webadmin@linux.intel.com
Location: United States of America

Repositories: 1
Profile: https://github.com/open-edge-platform

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: "Anomalib: A Deep Learning Library for Anomaly Detection"
message: "If you use this library and love it, cite the software and the paper \U0001F917"
authors:
  - given-names: Samet
    family-names: Akcay
    email: samet.akcay@intel.com
    affiliation: Intel
  - given-names: Dick
    family-names: Ameln
    email: dick.ameln@intel.com
    affiliation: Intel
  - given-names: Ashwin
    family-names: Vaidya
    email: ashwin.vaidya@intel.com
    affiliation: Intel
  - given-names: Barath
    family-names: Lakshmanan
    email: barath.lakshmanan@intel.com
    affiliation: Intel
  - given-names: Nilesh
    family-names: Ahuja
    email: nilesh.ahuja@intel.com
    affiliation: Intel
  - given-names: Utku
    family-names: Genc
    email: utku.genc@intel.com
    affiliation: Intel
version: 0.2.6
doi: https://doi.org/10.48550/arXiv.2202.08341
date-released: 2022-02-18
references:
  - type: article
    authors:
      - given-names: Samet
        family-names: Akcay
        email: samet.akcay@intel.com
        affiliation: Intel
      - given-names: Dick
        family-names: Ameln
        email: dick.ameln@intel.com
        affiliation: Intel
      - given-names: Ashwin
        family-names: Vaidya
        email: ashwin.vaidya@intel.com
        affiliation: Intel
      - given-names: Barath
        family-names: Lakshmanan
        email: barath.lakshmanan@intel.com
        affiliation: Intel
      - given-names: Nilesh
        family-names: Ahuja
        email: nilesh.ahuja@intel.com
        affiliation: Intel
      - given-names: Utku
        family-names: Genc
        email: utku.genc@intel.com
        affiliation: Intel
    title: "Anomalib: A Deep Learning Library for Anomaly Detection"
    year: 2022
    journal: ArXiv
    doi: https://doi.org/10.48550/arXiv.2202.08341
    url: https://arxiv.org/abs/2202.08341

abstract: >-
  This paper introduces anomalib, a novel library for
  unsupervised anomaly detection and localization.
  With reproducibility and modularity in mind, this
  open-source library provides algorithms from the
  literature and a set of tools to design custom
  anomaly detection algorithms via a plug-and-play
  approach. Anomalib comprises state-of-the-art
  anomaly detection algorithms that achieve top
  performance on the benchmarks and that can be used
  off-the-shelf. In addition, the library provides
  components to design custom algorithms that could
  be tailored towards specific needs. Additional
  tools, including experiment trackers, visualizers,
  and hyper-parameter optimizers, make it simple to
  design and implement anomaly detection models. The
  library also supports OpenVINO model optimization
  and quantization for real-time deployment. Overall,
  anomalib is an extensive library for the design,
  implementation, and deployment of unsupervised
  anomaly detection models from data to the edge.
keywords:
  - Unsupervised Anomaly detection
  - Unsupervised Anomaly localization
license: Apache-2.0

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Development Distribution Score (DDS): 0.628

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Name	Email	Commits
Samet Akcay	s**y@i**m	250
Ashwin Vaidya	a**a@i**m	155
Dick Ameln	d**n@i**m	83
Ashwin Vaidya	a**a@g**m	28
dependabot[bot]	4****]	24
Alexander Riedel	5****1	17
Blaž Rolih	6****r	15
Joao P C Bertoldo	2****o	13
abc-125	6****5	10
Adrian Boguszewski	a**i@i**m	10
ORippler	o**l@g**e	8
Yunchu Lee	y**e@i**m	7
Barath Lakshmanan	b**n@i**m	6
Harim Kang	h**g@i**m	6
Willy Fitra Hendria	w**a@g**m	5
Weilin Xu	w**u@i**m	5
Ashish	a**a@i**m	4
Wenjing Kang	w**g@i**m	4
Philippe Carvalho	3****l	4
Alexander Barabanov	9****v	3
Alexander Dokuchaev	a**v@i**m	3
David Muhr	d****n	3
Nilesh Ahuja	9****l	3
Sid Mehta	s**0@g**m	3
TRAN Triet	5****2	3
Tom Gambone	s****x	3
Paula Ramos	p**s@i**m	3
Danylo Boiko	5****o	2
Isaac Ng	4****z	2
Leonid Beynenson	l**n@i**m	2
and 56 more...

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pypi.org: anomalib

anomalib - Anomaly Detection Library

Documentation: https://anomalib.readthedocs.io/
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Latest release: 2.1.0
published 7 months ago

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Docker downloads count: 3.7%

Average: 4.8%

Dependent packages count: 4.8%

Dependent repos count: 9.0%

Maintainers (3)

samet-akcay ashwin-vaidya djdameln

Last synced: 6 months ago

Dependencies

.github/workflows/code_scan.yml actions

actions/checkout v3 composite
actions/setup-python v4 composite
actions/upload-artifact v3 composite

.github/workflows/docs.yml actions

actions/checkout v3 composite
actions/configure-pages v2 composite
actions/deploy-pages v1 composite
actions/setup-python v4 composite
actions/upload-pages-artifact v1 composite

.github/workflows/labeler.yml actions

actions/labeler v4 composite

.github/workflows/nightly.yml actions

actions/checkout v2 composite
actions/upload-artifact v2 composite

.github/workflows/pre_merge.yml actions

actions/checkout v2 composite
actions/checkout v3 composite
actions/setup-python v4 composite

.github/workflows/publish.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite

.github/workflows/upload_coverage.yml actions

actions/download-artifact v3 composite

.ci/Dockerfile docker

nvidia/cuda 11.4.3-devel-ubuntu20.04 build
python_base_cuda11.4 latest build

pyproject.toml pypi

requirements/base.txt pypi

albumentations >=1.1.0
av >=10.0.0
einops >=0.3.2
freia >=0.2
imgaug ==0.4.0
jsonargparse >=4.3
kornia >=0.6.6,<0.6.10
matplotlib >=3.4.3
omegaconf >=2.1.1
opencv-python >=4.5.3.56
pandas >=1.1.0
pytorch-lightning >=1.7.0,<1.10.0
setuptools >=41.0.0
timm >=0.5.4,<=0.6.12
torchmetrics ==0.10.3

requirements/dev.txt pypi

coverage * development
pre-commit * development
pytest * development
pytest-cov * development
pytest-order * development
pytest-sugar * development
pytest-xdist * development
tox * development

requirements/docs.txt pypi

furo ==2022.9.29
ipykernel *
myst-parser *
nbsphinx >=0.8.9
pandoc *
sphinx >=4.1.2
sphinx-autoapi *
sphinxemoji ==0.1.8

requirements/loggers.txt pypi

GitPython *
comet-ml >=3.31.7
gradio >=2.9.4
ipykernel *
tensorboard *
wandb ==0.12.17

requirements/notebooks.txt pypi

gitpython *
ipykernel *
ipywidgets *
notebook *

requirements/openvino.txt pypi

defusedxml ==0.7.1
networkx *
nncf >=2.1.0
onnx >=1.10.1
openvino-dev >=2022.3.0
requests >=2.26.0

setup.py pypi

anomalib

Science Score: 54.0%

Keywords

Keywords from Contributors

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

👋 Introduction

Key features

📦 Installation

🚀 Quick Install

With uv

Or with pip

🧠 Training

🔌 Python API

Initialize components

Train the model

⌨️ Command Line

Train with default settings

Train with custom category

Train with config file

🤖 Inference

🔌 Python API

Load model and make predictions

⌨️ Command Line

Basic prediction

Prediction with results

Training on Intel GPUs

🔌 API

⌨️ CLI

⚙️ Hyperparameter Optimization

Run HPO with Weights & Biases

🧪 Experiment Management

📊 Benchmarking

Run benchmarking with default configuration

✍️ Reference

👥 Contributing

Owner

Citation (CITATION.cff)

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pypi.org: anomalib

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Maintainers (3)

Dependencies