datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
mvtec_all_objects_splitsynthetic-mvtec-ad-defect-detection
Synthetic MVTec AD – Defect Detection Dataset by AnywayLabs.ai
Need a custom synthetic dataset for your own defect detection use case?
This dataset is an open-source sample of our synthetic data generation work at AnywayLabs.
If you're working on:
industrial defect detection
visual inspection
supervised anomaly detection
hard-to-collect defect classes
synthetic data for computer vision training
You can request a custom synthetic dataset here, or email:… See the full description on the dataset page: https://huggingface.co/datasets/anywaylabs/synthetic-mvtec-ad-defect-detection.mvtecmvtec_yolo
MVTec D2S Dataset
This dataset is a modification of the D2S set to the COCO format.
The Annotations for the test set are not public and the results can be requested per mail if in the correct format.
The Augmentation set (10k images) is split into Train and Val atm (70-30) but we should maybe change this as the val set is important since we don't have the annotations of the test set
mvtec_mapped
Mapped MVTec Sets
This Repo contains different test sets which are subsets of the actual MVTec Set but mapped to our labels and filtered.
mvtec_admvtec_backgroundsMVTecADTextImagePairsmvtec_depth_promptgenerated_mvtec_imagesmvtec_ad_oneshotmvtec_bottle_cls
task_categories:
image
Mvtec bottle cls
Dataset Description
MVTec AD Bottle dataset for anomaly detection using classification.
Overview
Data Type: image
Classes:
Version: 1.0.0
Outputs-Label Schema:
{'name': 'class_label', 'type': 'classification', 'classes': ['good', 'broken_large', 'broken_small', 'contamination'], 'value_range': None, 'required': False}
Data Count in splits:
train: 211
test: 85
mvtec_capsule_cls
task_categories:
image
Mvtec capsule cls
Dataset Description
MVTec AD Capsule dataset for anomaly detection using classification.
Overview
Data Type: image
Classes:
Version: 1.0.0
Outputs-Label Schema:
{'name': 'class_label', 'type': 'classification', 'classes': ['good', 'crack', 'faulty_imprint', 'poke', 'scratch', 'squeeze'], 'value_range': None, 'required': False}
Data Count in splits:
train: 221
test: 134
generated_mvtec_resolutionIVL_OOD_MVTec_overall