Improve dataset card: Add paper link, code link, task category, and detailed description (#2)

- Improve dataset card: Add paper link, code link, task category, and detailed description (47c5febb8da808840e8b80114f5c0cb66e009690)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,3 +1,50 @@
----
-license: cc-by-nc-4.0
----
+---
+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).