**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-2.10%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 v2.7.0 Release!** 🌟 > > This minor release adds new models and hardens checkpoint and path handling, while removing APIs scheduled for 2.7.0. > > Key Changes > > - **New models**: MH-PatchCore, RAD (retrieval-based), and FoundAD (few-shot). > - **Security / API**: Checkpoint loading defaults to `weights_only=True`. When a dataset `root` is set, metadata paths must resolve under that root. > - **Removals**: Legacy ONNX exporter (`dynamo=False`) is gone — use `dynamo=True` (default) with `anomalib[openvino]`. Pre-timm ViT checkpoint migration is no longer supported. Minimum PyTorch is 2.10+ (ROCm 2.13+). > > We value your input! Please share feedback via [GitHub Issues](https://github.com/open-edge-platform/anomalib/issues) or our [Discussions](https://github.com/open-edge-platform/anomalib/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, and benchmarking. - The largest public collection of ready-to-use deep learning anomaly detection algorithms and benchmark datasets. - [**Lightning**](https://www.lightning.ai/) 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**](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](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 12.6 support (Linux/Windows with NVIDIA GPU) uv pip install "anomalib[cu126]" # CUDA 13.0 support (Linux/Windows with NVIDIA GPU) uv pip install "anomalib[cu130]" # ROCm support (Linux with AMD GPU) uv pip install "anomalib[rocm]" # Intel XPU support (Linux/Windows with Intel GPU) uv pip install "anomalib[xpu]" ``` **Using `pip`:** The same extras can be used with `pip`: ```bash pip install "anomalib[cu130]" ```

🧩 Advanced Installation: Additional Dependencies

Anomalib includes most dependencies by default. For specialized features, you may need additional optional dependencies. Remember to include your hardware-specific extra. ```bash # Example: Install with OpenVINO support and CUDA 13.0 uv pip install "anomalib[openvino,cu130]" # Example: Install all optional dependencies for a CPU-only setup uv pip install "anomalib[full,cpu]" ``` Here is a list of available optional dependency groups: | Extra | Description | Purpose | | :-------------- | :--------------------------------------- | :------------------------------------------------------------------------------------ | | `[openvino]` | Intel OpenVINO optimization | For accelerated inference on Intel hardware | | `[clip]` | Vision-language models | `winclip` | | `[vlm]` | Vision-language model backends | Advanced VLM features | | `[loggers]` | Experiment tracking (wandb, comet, etc.) | For experiment management | | `[notebooks]` | Jupyter notebook support | For running example notebooks | | `[video]` | Video decoding | Video datasets such as Avenue | | `[huggingface]` | Hugging Face Hub and safetensors | Hub-backed models and checkpoints | | `[datasets]` | Dataset storage extras | Formats such as those that need `pyarrow` | | `[full]` | Optional feature dependencies | `clip`, `datasets`, `openvino`, `loggers`, `notebooks`, `vlm`, `video`, `huggingface` |

🔧 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 13.0 uv sync --extra cu130 # 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 \ --ckpt_path path/to/model.ckpt \ --return_predictions true ``` > 📘 **Note:** For advanced inference options including Gradio and OpenVINO, check our [Inference Documentation](https://anomalib.readthedocs.io). # 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](https://pytorch.org/docs/stable/notes/get_start_xpu.html) ## 🔌 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 ``` # 🧪 Experiment Management Track your experiments with popular logging platforms through [PyTorch Lightning loggers](https://lightning.ai/docs/pytorch/stable/extensions/logging.html): - 📊 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](https://anomalib.readthedocs.io/en/latest/markdown/guides/reference/loggers/index.html). # 📊 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](src/anomalib/models/image/patchcore/README.md#mvtec-ad-dataset) > - [Other Models](src/anomalib/models/) # Anomalib Studio > [!IMPORTANT] > Anomalib Studio is currently under active development and should be considered a pre-release. Features may change, and some functionality may be incomplete or unstable. We welcome feedback and contributions as we work towards a stable release. Anomalib Studio is a low/no-code web application that allows users to train and deploy anomaly detection models. It enables users to leverage Anomalib's features in their operational environment. Users can connect USB and IP cameras, or use a folder of images, as input to the training pipeline. The tool allows direct output to their industrial pipelines through ROS messages, MQTT, etc.

Anomalib Studio

The source code for Anomalib Studio lives in the [application](application) folder. Anomalib Studio is available as two distributions: 1. As a [docker container](application/docker) 2. As a [standalone application](application/ui) For more information on each, refer to the respective README files. ## Get started with development build ### Setup Backend Dependencies > [!NOTE] > This assumes that you have `uv` installed. If not, please refer to the [installation guide](https://docs.astral.sh/uv/getting-started/installation/). ```bash cd application/backend uv sync --extra xpu # or uv sync --extra cu130 for CUDA 13.0, uv sync --extra cpu for CPU ``` ### Setup Frontend Dependencies ```bash cd application/ui npm install ``` ### Run the application > [!IMPORTANT] > Both backend and frontend dependencies should be installed before running the application. Run the backend server: ```bash cd application/backend ./run.sh ``` Run the frontend server: ```bash cd application/ui npm run start ``` Navigate to [http://localhost:3000](http://localhost:3000) to see the application. # ✍️ 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](CONTRIBUTING.md) to get started.

Thank you to all our contributors!