--- license: cc-by-nc-4.0 task_categories: - image-segmentation tags: - anomaly-detection - continual-learning - benchmark --- # Continual-MEGA: A Large-scale Benchmark for Generalizable Continual Anomaly Detection This repository contains the dataset for **Continual-MEGA**, a new benchmark for continual learning in anomaly detection, introduced in the paper [Continual-MEGA: A Large-scale Benchmark for Generalizable Continual Anomaly Detection](https://huggingface.co/papers/2506.00956). Continual-MEGA aims to better reflect real-world deployment scenarios. It features a large and diverse dataset that significantly expands existing evaluation settings by combining carefully curated existing datasets with the newly proposed **ContinualAD** dataset. The benchmark also proposes a novel scenario for measuring zero-shot generalization to unseen classes, particularly focusing on pixel-level defect localization. For the associated evaluation code, checkpoint files, and further details, please refer to the GitHub repository: [https://github.com/Continual-Mega/Continual-Mega-Neurips2025](https://github.com/Continual-Mega/Continual-Mega-Neurips2025) ## Dataset Structure The Continual-MEGA benchmark dataset combines data from various sources, structured as follows: ``` data/ ├── continual_ad/ # Our proposed ContinualAD dataset ├── mvtec_anomaly_detection/ # MVTec-AD dataset ├── VisA_20220922/ # VisA dataset ├── VIADUCT/ # VIADUCT dataset ├── Real-IAD-512/ # RealIAD dataset (512 size) ├── MPDD/ # MPDD dataset └── BTAD/ # BTAD ``` ## Sample Usage (Evaluation) The evaluation code for the Continual-MEGA benchmark is available in the associated GitHub repository. After cloning the repository and setting up, you can run the following commands: ### Continual Settings Evaluation ```bash sh eval_continual.sh ``` ### Zero-Shot Generalization Evaluation ```bash sh eval_zero.sh ``` For detailed setup instructions, including downloading CLIP pretrained weights and specific checkpoint files, please visit the [official GitHub repository](https://github.com/Continual-Mega/Continual-Mega-Neurips2025).