Hammadhaideerr/CTTA-AD-Benchmarks

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

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:

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