BrachioLab/visa

1---2dataset_info:3  features:4  - name: image5    dtype: image6  - name: mask7    dtype: image8  - name: label9    dtype: int6410  splits:11  - name: candle.train12    num_bytes: 106451773.013    num_examples: 90014  - name: candle.test15    num_bytes: 23359449.016    num_examples: 20017  - name: capsules.train18    num_bytes: 133021141.019    num_examples: 54220  - name: capsules.test21    num_bytes: 39865980.022    num_examples: 16023  - name: cashew.train24    num_bytes: 135528457.025    num_examples: 45026  - name: cashew.test27    num_bytes: 48713873.028    num_examples: 15029  - name: chewinggum.train30    num_bytes: 63491934.031    num_examples: 45332  - name: chewinggum.test33    num_bytes: 21472874.034    num_examples: 15035  - name: fryum.train36    num_bytes: 63780392.037    num_examples: 45038  - name: fryum.test39    num_bytes: 21646212.040    num_examples: 15041  - name: macaroni1.train42    num_bytes: 105415318.043    num_examples: 90044  - name: macaroni1.test45    num_bytes: 24090768.046    num_examples: 20047  - name: macaroni2.train48    num_bytes: 100349144.049    num_examples: 90050  - name: macaroni2.test51    num_bytes: 22470288.052    num_examples: 20053  - name: pcb1.train54    num_bytes: 244978923.055    num_examples: 90456  - name: pcb1.test57    num_bytes: 53521326.058    num_examples: 20059  - name: pcb2.train60    num_bytes: 224276308.061    num_examples: 90162  - name: pcb2.test63    num_bytes: 51075179.064    num_examples: 20065  - name: pcb3.train66    num_bytes: 127418394.067    num_examples: 90568  - name: pcb3.test69    num_bytes: 28467534.070    num_examples: 20171  - name: pcb4.train72    num_bytes: 192400641.073    num_examples: 90474  - name: pcb4.test75    num_bytes: 44329307.076    num_examples: 20177  - name: pipe_fryum.train78    num_bytes: 42230565.079    num_examples: 45080  - name: pipe_fryum.test81    num_bytes: 14593580.082    num_examples: 15083  download_size: 191790607384  dataset_size: 1932949360.085configs:86- config_name: default87  data_files:88  - split: candle.train89    path: data/candle.train-*90  - split: candle.test91    path: data/candle.test-*92  - split: capsules.train93    path: data/capsules.train-*94  - split: capsules.test95    path: data/capsules.test-*96  - split: cashew.train97    path: data/cashew.train-*98  - split: cashew.test99    path: data/cashew.test-*100  - split: chewinggum.train101    path: data/chewinggum.train-*102  - split: chewinggum.test103    path: data/chewinggum.test-*104  - split: fryum.train105    path: data/fryum.train-*106  - split: fryum.test107    path: data/fryum.test-*108  - split: macaroni1.train109    path: data/macaroni1.train-*110  - split: macaroni1.test111    path: data/macaroni1.test-*112  - split: macaroni2.train113    path: data/macaroni2.train-*114  - split: macaroni2.test115    path: data/macaroni2.test-*116  - split: pcb1.train117    path: data/pcb1.train-*118  - split: pcb1.test119    path: data/pcb1.test-*120  - split: pcb2.train121    path: data/pcb2.train-*122  - split: pcb2.test123    path: data/pcb2.test-*124  - split: pcb3.train125    path: data/pcb3.train-*126  - split: pcb3.test127    path: data/pcb3.test-*128  - split: pcb4.train129    path: data/pcb4.train-*130  - split: pcb4.test131    path: data/pcb4.test-*132  - split: pipe_fryum.train133    path: data/pipe_fryum.train-*134  - split: pipe_fryum.test135    path: data/pipe_fryum.test-*136---137 138 139Original dataset:140 141```142@inproceedings{zou2022spot,143  title={Spot-the-difference self-supervised pre-training for anomaly detection and segmentation},144  author={Zou, Yang and Jeong, Jongheon and Pemula, Latha and Zhang, Dongqing and Dabeer, Onkar},145  booktitle={European Conference on Computer Vision},146  pages={392--408},147  year={2022},148  organization={Springer}149}150```151