Datasets:
Modalities:
Image
Formats:
imagefolder
Languages:
English
Size:
10K - 100K
Tags:
anomaly-detection
continual-learning
test-time-adaptation
medical-imaging
industrial-inspection
License:
File size: 2,534 Bytes
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license: cc-by-nc-sa-4.0
task_categories:
- image-classification
- image-segmentation
language:
- en
tags:
- anomaly-detection
- continual-learning
- test-time-adaptation
- medical-imaging
- industrial-inspection
size_categories:
- 10K<n<100K
---
# 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 |