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README.md

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----
-license: other
-license_name: cc-by-nc-sa-4.0
-license_link: LICENSE
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+# CTTA-AD Benchmarks
+
+Dataset collection for **CTTA-AD: Continual Test-Time Adaptation for Unified Few-Shot Visual Anomaly Detection** (AAAI 2027 submission).
+
+## Datasets
+
+| Dataset | Domain | Categories | Train Normal | License |
+|---|---|---|---|---|
+| MVTec-AD | Industrial | 15 | 209–391 per category | CC BY-NC-SA 4.0 |
+| VisA | Industrial | 12 | 400–905 per category | CC BY-NC-SA 4.0 |
+| MVTec-LOCO | Logical | 5 | varies | CC BY-NC-SA 4.0 |
+| BrainMRI | Medical | 1 | 7,500 | Research only |
+| LiverCT | Medical | 1 | 1,542 | Research only |
+| RESC | Medical | 1 | 4,297 | Research only |
+| HIS | Medical | 1 | 5,088 | Research only |
+| OCT17 | Medical | 1 | 11,017 | Research only |
+| ChestXray | Medical | 1 | 100 | Research only |
+
+## Folder Structure
+
+All datasets follow this unified format:
+
+    DatasetName/
+    ├── category_name/
+    │   ├── train/
+    │   │   └── good/               # normal training images
+    │   ├── test/
+    │   │   ├── good/               # normal test images
+    │   │   └── <defect_type>/      # anomalous test images
+    │   └── ground_truth/           # pixel-level masks (where available)
+
+**Notes:**
+- Medical datasets use `Ungood` as the anomaly folder name
+- OCT17 train is split into `good_a` and `good_b` (>10k files)
+- ChestXray anomaly is split into `Ungood_a` and `Ungood_b` (>10k files)
+
+## Download and Setup
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="Hammadhaideerr/CTTA-AD-Benchmarks",
+    repo_type="dataset",
+    local_dir="data/",
+)
+```
+
+Or download a single dataset:
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    repo_id="Hammadhaideerr/CTTA-AD-Benchmarks",
+    repo_type="dataset",
+    local_dir="data/BrainMRI/",
+    allow_patterns="BrainMRI/*",
+)
+```
+
+## Citation
+
+If you use these datasets, please cite the original dataset papers alongside our work:
+
+- **MVTec-AD:** Bergmann et al., CVPR 2019
+- **VisA:** Zou et al., ECCV 2022
+- **MVTec-LOCO:** Bergmann et al., IJCV 2022
+- **BMAD (BrainMRI, LiverCT, RESC, HIS, OCT17, ChestXray):** Bao et al., CVPR Workshops 2024