| --- |
| license: cc-by-4.0 |
| task_categories: |
| - image-classification |
| - image-segmentation |
| tags: |
| - anomaly-detection |
| - industrial |
| - visual-inspection |
| - defect-detection |
| pretty_name: VisA (Visual Anomaly) |
| --- |
| |
| # VisA — Visual Anomaly Dataset |
|
|
| Mirror of the VisA (Visual Anomaly) dataset for research use. Staged as a proxy/pretraining |
| dataset for CoRe's Situational Control paint-inspection work (`core-lab/situational-control`). |
|
|
| ## Source |
|
|
| - Original repo: https://github.com/amazon-science/spot-diff |
| - Original download: https://amazon-visual-anomaly.s3.us-west-2.amazonaws.com/VisA_20220922.tar |
| - License: **CC BY 4.0** (confirmed in the source repo README) |
| |
| ## Contents |
| |
| 10,821 images across 12 object subsets (candle, capsules, cashew, chewinggum, fryum, macaroni1, |
| macaroni2, pcb1-4, pipe_fryum), 9,621 normal + 1,200 anomalous. Pixel-level segmentation masks |
| included for anomalous images. Structure preserved as-is from the original tar: |
|
|
| ``` |
| <object>/Data/Images/{Normal,Anomaly}/*.JPG |
| <object>/Data/Masks/Anomaly/*.png |
| <object>/image_anno.csv |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{zou2022spot, |
| title={SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation}, |
| author={Zou, Yang and Jeong, Jongheon and Pemula, Latha and Zhang, Dongqing and Dabeer, Onkar}, |
| journal={arXiv preprint arXiv:2207.14315}, |
| year={2022} |
| } |
| ``` |
|
|
| Paper: "SPot-the-Difference Self-Supervised Pre-training for Anomaly Detection and Segmentation", |
| Zou et al., ECCV 2022. |
|
|
| All credit for data collection and annotation belongs to the original authors (Amazon Science). |
| This is an unmodified mirror for convenience of access. |
|
|
