Anomalib Project Overview
Anomalib is a comprehensive deep learning library designed for anomaly detection. Its main aim is to offer a collection of cutting-edge algorithms optimized for identifying abnormalities in both public and private datasets. The library predominantly focuses on visual anomaly detection, which targets anomalies in images or videos. Anomalib is continually updated with fresh algorithms and new extensions for training and inference, ensuring its users have access to the latest technological advancements.
Key Features
- User-friendly API and CLI: Anomalib provides both an API and Command Line Interface for training, inference, and other tasks, making the library accessible to users with different preferences.
- Extensive Algorithm Collection: It boasts the largest collection of ready-to-use deep learning anomaly detection algorithms and benchmark datasets available publicly.
- Lightning Framework Integration: Implementation of models using the Lightning framework helps reduce the amount of repetitive code, allowing developers to focus on essential tasks.
- OpenVINO Support: Most models can be exported to OpenVINO Intermediate Representation (IR) for enhanced inference performance on Intel hardware.
- Deployment Tools: Anomalib includes a suite of tools to quickly and easily deploy both standard and customized anomaly detection models.
Installation
Anomalib can be installed in two ways:
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Via PyPI: Ideal for users who don’t need to alter the source code.
pip install anomalib
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From Source: Recommended for developers planning to modify the library.
git clone https://github.com/openvinotoolkit/anomalib.git cd anomalib pip install -e .
Training Models
Anomalib supports both API-based and CLI-based training methods:
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API-Based Training: Offers greater flexibility and customization. For example, using the provided tools, a simple API script can be written to load datasets and models, then train the model using Anomalib's Engine.
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CLI-Based Training: Suitable for users preferring pre-configured options without detailed customization.
Inference
Anomalib includes multiple inferencing scripts suitable for different environments, including Torch, Lightning, Gradio, and OpenVINO. For instance, using the Lightning inferencer allows users to make predictions from a model checkpoint using a simple API call.
Hyperparameter Optimization
Users can perform hyperparameter optimization using frameworks like Weights & Biases (wandb) and Comet.ml, helping models achieve peak performance through fine-tuned parameter adjustments.
Experiment Management
Anomalib integrates with experiment tracking systems such as Comet, TensorBoard, and wandb, enabling efficient experiment management and tracking of metrics, hyper-parameters, and more.
Benchmarking
The library offers benchmarking tools to evaluate model performance on various datasets. This feature helps determine the effectiveness of different models and configurations on anomaly detection tasks.
Contribution and Community
Anomalib welcomes contributions from the community. Contributors are encouraged to review the CONTRIBUTING.md guidelines. The project values and appreciates the continuous support and input from its community to ensure it remains robust and up-to-date.
For anyone interested in cutting-edge anomaly detection, Anomalib provides a powerful, flexible, and comprehensive resource.