base on An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference. <div align="center"> <img src="https://raw.githubusercontent.com/open-edge-platform/anomalib/main/docs/source/_static/images/logos/anomalib-wide-blue.png" width="600px" alt="Anomalib Logo - A deep learning library for anomaly detection"> **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://www.bestpractices.dev/projects/8330) [](https://anomalib.readthedocs.io/en/latest/?badge=latest) [](https://gurubase.io/g/anomalib) <a href="https://trendshift.io/repositories/6030" target="_blank"><img src="https://trendshift.io/api/badge/repositories/6030" alt="open-edge-platform%2Fanomalib | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a> </div> --- > 🌟 **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](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! <p align="center"> <img src="https://raw.githubusercontent.com/open-edge-platform/anomalib/main/docs/source/_static/images/readme.png" width="1000" alt="A prediction made by anomalib"> </p> ## 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**](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. <details> <summary><strong>💡 Advanced Installation: Specify Hardware Backend</strong></summary> 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]" ``` </details> <details> <summary><strong>🧩 Advanced Installation: Additional Dependencies</strong></summary> 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 12.4 uv pip install "anomalib[openvino,cu124]" # 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 | | `[full]` | All optional dependencies | All optional features | </details> <details> <summary><strong>🔧 Advanced Installation: Install from Source</strong></summary> 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]" ``` </details> # 🧠 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 ``` > 📘 **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 ``` # ⚙️ Hyperparameter Optimization Anomalib supports hyperparameter optimization (HPO) using [Weights & Biases](https://wandb.ai/) and [Comet.ml](https://www.comet.com/). ```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](https://open-edge-platform.github.io/anomalib/tutorials/hyperparameter_optimization.html). # 🧪 Experiment Management Track your experiments with popular logging platforms through [PyTorch Lightning loggers](https://pytorch-lightning.readthedocs.io/en/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://open-edge-platform.github.io/anomalib/tutorials/logging.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/) # ✍️ 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. <p align="center"> <a href="https://github.com/open-edge-platform/anomalib/graphs/contributors"> <img src="https://contrib.rocks/image?repo=open-edge-platform/anomalib" alt="Contributors to open-edge-platform/anomalib" /> </a> </p> <p align="center"> <b>Thank you to all our contributors!</b> </p> ", Assign "at most 3 tags" to the expected json: {"id":"14514","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"