**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) []() []() []() []() [](https://github.com/open-edge-platform/anomalib/actions/workflows/pre_merge.yml) [](https://codecov.io/gh/open-edge-platform/anomalib) [](https://pepy.tech/project/anomalib) [](https://snyk.io/advisor/python/anomalib) [](https://anomalib.readthedocs.io/en/latest/?badge=latest) [](https://gurubase.io/g/anomalib)

🌟 Announcing v2.0.0 Release! 🌟

We're excited to announce the release of Anomalib v2.0.0! This version introduces significant improvements and customization options to enhance your anomaly detection workflows. Please be aware that there are several API changes between v1.2.0 and v2.0.0, so please be careful when updating your existing pipelines. Key features include:

• Multi-GPU support
• New dataclasses for model in- and outputs.
• Flexible configuration of model transforms and data augmentations.
• Configurable modules for pre- and post-processing operations via Preprocessor and Postprocessor
• Customizable model evaluation workflow with new Metrics API and Evaluator module.
• Configurable module for visualization via Visualizer (docs guide: coming soon)

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!

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 provides multiple installation options to suit your needs. Choose the one that best fits your requirements:

🚀 Install from PyPI

```bash

Basic installation from PyPI

pip install anomalib

Full installation with all dependencies

pip install anomalib[full] ```

🔧 Install from Source

For contributing or customizing the library:

```bash git clone https://github.com/open-edge-platform/anomalib.git cd anomalib pip install -e .

Full development installation with all dependencies

pip install -e .[full] ```

🧠 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/benchmarking/benchmark_params.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!