metadata
dataset_info:
features:
- name: image
dtype: image
- name: mask
dtype: image
- name: label
dtype: int64
splits:
- name: candle.train
num_bytes: 106451773
num_examples: 900
- name: candle.test
num_bytes: 23359449
num_examples: 200
- name: capsules.train
num_bytes: 133021141
num_examples: 542
- name: capsules.test
num_bytes: 39865980
num_examples: 160
- name: cashew.train
num_bytes: 135528457
num_examples: 450
- name: cashew.test
num_bytes: 48713873
num_examples: 150
- name: chewinggum.train
num_bytes: 63491934
num_examples: 453
- name: chewinggum.test
num_bytes: 21472874
num_examples: 150
- name: fryum.train
num_bytes: 63780392
num_examples: 450
- name: fryum.test
num_bytes: 21646212
num_examples: 150
- name: macaroni1.train
num_bytes: 105415318
num_examples: 900
- name: macaroni1.test
num_bytes: 24090768
num_examples: 200
- name: macaroni2.train
num_bytes: 100349144
num_examples: 900
- name: macaroni2.test
num_bytes: 22470288
num_examples: 200
- name: pcb1.train
num_bytes: 244978923
num_examples: 904
- name: pcb1.test
num_bytes: 53521326
num_examples: 200
- name: pcb2.train
num_bytes: 224276308
num_examples: 901
- name: pcb2.test
num_bytes: 51075179
num_examples: 200
- name: pcb3.train
num_bytes: 127418394
num_examples: 905
- name: pcb3.test
num_bytes: 28467534
num_examples: 201
- name: pcb4.train
num_bytes: 192400641
num_examples: 904
- name: pcb4.test
num_bytes: 44329307
num_examples: 201
- name: pipe_fryum.train
num_bytes: 42230565
num_examples: 450
- name: pipe_fryum.test
num_bytes: 14593580
num_examples: 150
download_size: 1917906073
dataset_size: 1932949360
configs:
- config_name: default
data_files:
- split: candle.train
path: data/candle.train-*
- split: candle.test
path: data/candle.test-*
- split: capsules.train
path: data/capsules.train-*
- split: capsules.test
path: data/capsules.test-*
- split: cashew.train
path: data/cashew.train-*
- split: cashew.test
path: data/cashew.test-*
- split: chewinggum.train
path: data/chewinggum.train-*
- split: chewinggum.test
path: data/chewinggum.test-*
- split: fryum.train
path: data/fryum.train-*
- split: fryum.test
path: data/fryum.test-*
- split: macaroni1.train
path: data/macaroni1.train-*
- split: macaroni1.test
path: data/macaroni1.test-*
- split: macaroni2.train
path: data/macaroni2.train-*
- split: macaroni2.test
path: data/macaroni2.test-*
- split: pcb1.train
path: data/pcb1.train-*
- split: pcb1.test
path: data/pcb1.test-*
- split: pcb2.train
path: data/pcb2.train-*
- split: pcb2.test
path: data/pcb2.test-*
- split: pcb3.train
path: data/pcb3.train-*
- split: pcb3.test
path: data/pcb3.test-*
- split: pcb4.train
path: data/pcb4.train-*
- split: pcb4.test
path: data/pcb4.test-*
- split: pipe_fryum.train
path: data/pipe_fryum.train-*
- split: pipe_fryum.test
path: data/pipe_fryum.test-*
Original dataset:
@inproceedings{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},
booktitle={European Conference on Computer Vision},
pages={392--408},
year={2022},
organization={Springer}
}