| Name | anomalib JSON |
| Version |
2.2.0
JSON |
| download |
| home_page | None |
| Summary | anomalib - Anomaly Detection Library |
| upload_time | 2025-10-09 11:25:25 |
| maintainer | None |
| docs_url | None |
| author | Intel OpenVINO |
| requires_python | >=3.10 |
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<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>
Raw data
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"name": "anomalib",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10",
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"keywords": null,
"author": "Intel OpenVINO",
"author_email": null,
"download_url": "https://files.pythonhosted.org/packages/7a/c9/7b99ed9bc710ae986c5e53c7cbe66ffb076a99155beacacc478c760d87f7/anomalib-2.2.0.tar.gz",
"platform": null,
"description": "<div align=\"center\">\n\n<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\">\n\n**A library for benchmarking, developing and deploying deep learning anomaly detection algorithms**\n\n---\n\n[Key Features](#key-features) \u2022\n[Docs](https://anomalib.readthedocs.io/en/latest/) \u2022\n[Notebooks](examples/notebooks) \u2022\n[License](LICENSE)\n\n[]()\n[]()\n[]()\n[]()\n\n[](https://github.com/open-edge-platform/anomalib/actions/workflows/pre_merge.yml)\n[](https://codecov.io/gh/open-edge-platform/anomalib)\n[](https://pepy.tech/project/anomalib)\n[](https://snyk.io/advisor/python/anomalib)\n[](https://www.bestpractices.dev/projects/8330)\n\n[](https://anomalib.readthedocs.io/en/latest/?badge=latest)\n[](https://gurubase.io/g/anomalib)\n\n<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>\n\n</div>\n\n---\n\n> \ud83c\udf1f **Announcing v2.1.0 Release!** \ud83c\udf1f\n>\n> We're excited to announce the release of Anomalib v2.1.0!\n> This version brings several state-of-the-art models and anomaly detection datasets. Key features include:\n>\n> New models :\n>\n> - **\ud83d\uddbc\ufe0f UniNet (CVPR 2025)**: A contrastive learning-guided unified framework with feature selection for anomaly detection.\n> - **\ud83d\uddbc\ufe0f Dinomaly (CVPR 2025)**: A 'less is more philosophy' encoder-decoder architecture model leveraging pre-trained foundational models.\n> - **\ud83c\udfa5 Fuvas (ICASSP 2025)**: Few-shot unsupervised video anomaly segmentation via low-rank factorization of spatio-temporal features.\n>\n> New datasets:\n>\n> - **MVTec AD 2** : A new version of the MVTec AD dataset with 8 categories of industrial anomaly detection.\n> - **MVTec LOCO AD** : MVTec logical constraints anomaly detection dataset that includes both structural and logical anomalies.\n> - **Real-IAD** : A real-world multi-view dataset for benchmarking versatile industrial anomaly detection.\n> - **VAD dataset** : Valeo Anomaly Dataset (VAD) showcasing a diverse range of defects, from highly obvious to extremely subtle.\n>\n> 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)\n\n# \ud83d\udc4b Introduction\n\nAnomalib 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!\n\n<p align=\"center\">\n <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\">\n</p>\n\n## Key features\n\n- Simple and modular API and CLI for training, inference, benchmarking, and hyperparameter optimization.\n- The largest public collection of ready-to-use deep learning anomaly detection algorithms and benchmark datasets.\n- [**Lightning**](https://www.lightning.ai/) based model implementations to reduce boilerplate code and limit the implementation efforts to the bare essentials.\n- 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.\n- A set of [inference tools](tools) for quick and easy deployment of the standard or custom anomaly detection models.\n\n# \ud83d\udce6 Installation\n\nAnomalib can be installed from PyPI. We recommend using a virtual environment and a modern package installer like `uv` or `pip`.\n\n## \ud83d\ude80 Quick Install\n\nFor 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.\n\n```bash\n# With uv\nuv pip install anomalib\n\n# Or with pip\npip install anomalib\n```\n\nFor 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.\n\n<details>\n<summary><strong>\ud83d\udca1 Advanced Installation: Specify Hardware Backend</strong></summary>\n\nTo 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.\n\n**Using `uv`:**\n\n```bash\n# CPU support (default, works on all platforms)\nuv pip install \"anomalib[cpu]\"\n\n# CUDA 12.4 support (Linux/Windows with NVIDIA GPU)\nuv pip install \"anomalib[cu124]\"\n\n# CUDA 12.1 support (Linux/Windows with NVIDIA GPU)\nuv pip install \"anomalib[cu121]\"\n\n# CUDA 11.8 support (Linux/Windows with NVIDIA GPU)\nuv pip install \"anomalib[cu118]\"\n\n# ROCm support (Linux with AMD GPU)\nuv pip install \"anomalib[rocm]\"\n\n# Intel XPU support (Linux with Intel GPU)\nuv pip install \"anomalib[xpu]\"\n```\n\n**Using `pip`:**\nThe same extras can be used with `pip`:\n\n```bash\npip install \"anomalib[cu124]\"\n```\n\n</details>\n\n<details>\n<summary><strong>\ud83e\udde9 Advanced Installation: Additional Dependencies</strong></summary>\n\nAnomalib includes most dependencies by default. For specialized features, you may need additional optional dependencies. Remember to include your hardware-specific extra.\n\n```bash\n# Example: Install with OpenVINO support and CUDA 12.4\nuv pip install \"anomalib[openvino,cu124]\"\n\n# Example: Install all optional dependencies for a CPU-only setup\nuv pip install \"anomalib[full,cpu]\"\n```\n\nHere is a list of available optional dependency groups:\n\n| Extra | Description | Purpose |\n| :------------ | :--------------------------------------- | :------------------------------------------ |\n| `[openvino]` | Intel OpenVINO optimization | For accelerated inference on Intel hardware |\n| `[clip]` | Vision-language models | `winclip` |\n| `[vlm]` | Vision-language model backends | Advanced VLM features |\n| `[loggers]` | Experiment tracking (wandb, comet, etc.) | For experiment management |\n| `[notebooks]` | Jupyter notebook support | For running example notebooks |\n| `[full]` | All optional dependencies | All optional features |\n\n</details>\n\n<details>\n<summary><strong>\ud83d\udd27 Advanced Installation: Install from Source</strong></summary>\n\nFor contributing to `anomalib` or using a development version, you can install from source.\n\n**Using `uv`:**\nThis is the recommended method for developers as it uses the project's lock file for reproducible environments.\n\n```bash\ngit clone https://github.com/open-edge-platform/anomalib.git\ncd anomalib\n\n# Create the virtual environment\nuv venv\n\n# Sync with the lockfile for a specific backend (e.g., CPU)\nuv sync --extra cpu\n\n# Or for a different backend like CUDA 12.4\nuv sync --extra cu124\n\n# To set up a full development environment\nuv sync --extra dev --extra cpu\n```\n\n**Using `pip`:**\n\n```bash\ngit clone https://github.com/open-edge-platform/anomalib.git\ncd anomalib\n\n# Install in editable mode with a specific backend\npip install -e \".[cpu]\"\n\n# Install with development dependencies\npip install -e \".[dev,cpu]\"\n```\n\n</details>\n\n# \ud83e\udde0 Training\n\nAnomalib supports both API and CLI-based training approaches:\n\n## \ud83d\udd0c Python API\n\n```python\nfrom anomalib.data import MVTecAD\nfrom anomalib.models import Patchcore\nfrom anomalib.engine import Engine\n\n# Initialize components\ndatamodule = MVTecAD()\nmodel = Patchcore()\nengine = Engine()\n\n# Train the model\nengine.fit(datamodule=datamodule, model=model)\n```\n\n## \u2328\ufe0f Command Line\n\n```bash\n# Train with default settings\nanomalib train --model Patchcore --data anomalib.data.MVTecAD\n\n# Train with custom category\nanomalib train --model Patchcore --data anomalib.data.MVTecAD --data.category transistor\n\n# Train with config file\nanomalib train --config path/to/config.yaml\n```\n\n# \ud83e\udd16 Inference\n\nAnomalib provides multiple inference options including Torch, Lightning, Gradio, and OpenVINO. Here's how to get started:\n\n## \ud83d\udd0c Python API\n\n```python\n# Load model and make predictions\npredictions = engine.predict(\n datamodule=datamodule,\n model=model,\n ckpt_path=\"path/to/checkpoint.ckpt\",\n)\n```\n\n## \u2328\ufe0f Command Line\n\n```bash\n# Basic prediction\nanomalib predict --model anomalib.models.Patchcore \\\n --data anomalib.data.MVTecAD \\\n --ckpt_path path/to/model.ckpt\n\n# Prediction with results\nanomalib predict --model anomalib.models.Patchcore \\\n --data anomalib.data.MVTecAD \\\n --ckpt_path path/to/model.ckpt \\\n --return_predictions\n```\n\n> \ud83d\udcd8 **Note:** For advanced inference options including Gradio and OpenVINO, check our [Inference Documentation](https://anomalib.readthedocs.io).\n\n# Training on Intel GPUs\n\n> [!Note]\n> Currently, only single GPU training is supported on Intel GPUs.\n> These commands were tested on Arc 750 and Arc 770.\n\nEnsure 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)\n\n## \ud83d\udd0c API\n\n```python\nfrom anomalib.data import MVTecAD\nfrom anomalib.engine import Engine, SingleXPUStrategy, XPUAccelerator\nfrom anomalib.models import Stfpm\n\nengine = Engine(\n strategy=SingleXPUStrategy(),\n accelerator=XPUAccelerator(),\n)\nengine.train(Stfpm(), datamodule=MVTecAD())\n```\n\n## \u2328\ufe0f CLI\n\n```bash\nanomalib train --model Padim --data MVTecAD --trainer.accelerator xpu --trainer.strategy xpu_single\n```\n\n# \u2699\ufe0f Hyperparameter Optimization\n\nAnomalib supports hyperparameter optimization (HPO) using [Weights & Biases](https://wandb.ai/) and [Comet.ml](https://www.comet.com/).\n\n```bash\n# Run HPO with Weights & Biases\nanomalib hpo --backend WANDB --sweep_config tools/hpo/configs/wandb.yaml\n```\n\n> \ud83d\udcd8 **Note:** For detailed HPO configuration, check our [HPO Documentation](https://open-edge-platform.github.io/anomalib/tutorials/hyperparameter_optimization.html).\n\n# \ud83e\uddea Experiment Management\n\nTrack your experiments with popular logging platforms through [PyTorch Lightning loggers](https://pytorch-lightning.readthedocs.io/en/stable/extensions/logging.html):\n\n- \ud83d\udcca Weights & Biases\n- \ud83d\udcc8 Comet.ml\n- \ud83d\udcc9 TensorBoard\n\nEnable logging in your config file to track:\n\n- Hyperparameters\n- Metrics\n- Model graphs\n- Test predictions\n\n> \ud83d\udcd8 **Note:** For logging setup, see our [Logging Documentation](https://open-edge-platform.github.io/anomalib/tutorials/logging.html).\n\n# \ud83d\udcca Benchmarking\n\nEvaluate and compare model performance across different datasets:\n\n```bash\n# Run benchmarking with default configuration\nanomalib benchmark --config tools/experimental/benchmarking/sample.yaml\n```\n\n> \ud83d\udca1 **Tip:** Check individual model performance in their respective README files:\n>\n> - [Patchcore Results](src/anomalib/models/image/patchcore/README.md#mvtec-ad-dataset)\n> - [Other Models](src/anomalib/models/)\n\n# \u270d\ufe0f Reference\n\nIf you find Anomalib useful in your research or work, please cite:\n\n```tex\n@inproceedings{akcay2022anomalib,\n title={Anomalib: A deep learning library for anomaly detection},\n author={Akcay, Samet and Ameln, Dick and Vaidya, Ashwin and Lakshmanan, Barath and Ahuja, Nilesh and Genc, Utku},\n booktitle={2022 IEEE International Conference on Image Processing (ICIP)},\n pages={1706--1710},\n year={2022},\n organization={IEEE}\n}\n```\n\n# \ud83d\udc65 Contributing\n\nWe welcome contributions! Check out our [Contributing Guide](CONTRIBUTING.md) to get started.\n\n<p align=\"center\">\n <a href=\"https://github.com/open-edge-platform/anomalib/graphs/contributors\">\n <img src=\"https://contrib.rocks/image?repo=open-edge-platform/anomalib\" alt=\"Contributors to open-edge-platform/anomalib\" />\n </a>\n</p>\n\n<p align=\"center\">\n <b>Thank you to all our contributors!</b>\n</p>\n",
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