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# Albumentations Integration for Anomaly Detection In this notebook, we will explore **FiftyOne’s integration with Albumentations**, a powerful image augmentation library. Augmentations can significantly enhance **anomaly detection** models by improving robustness and generalization.  ## Learning Objectives: - Understand the importance of **data augmentation** for anomaly detection. - Use **Albumentations** to apply transformations to datasets in FiftyOne. - Explore different augmentation techniques for improving model performance. ## Why Use Augmentations for Anomaly Detection? Anomaly detection models often struggle due to **limited data availability** and **environmental variations**. Data augmentation helps by: - **Simulating real-world variations** (e.g., lighting changes, noise, blur). - **Increasing dataset diversity**, reducing overfitting. - **Improving model robustness** to unseen conditions. Albumentations allows us to apply **realistic transformations**, such as: - **Brightness and contrast adjustments** (simulate different lighting conditions). - **Color jittering** (alter hue, saturation, and intensity). - **Gaussian noise, blur, and distortions** (improve generalization). **Relevant Documentation:** - [Albumentations Documentation](https://albumentations.ai/docs/) - [Why Data Augmentation Matters](https://docs.voxel51.com/integrations/albumentations.html) ### How to manipulate Plugins [Here](https://docs.voxel51.com/plugins/using_plugins.html#managing-plugins) is a guide you can follow to check your plugins, enable, disable or delete those.