Contamination Map Methodology

Vision / Dataset

MPDD

Dataset for visual defect detection in industrial (painted) metal parts manufacturing: more than 1000 images with pixel-precise defect annotation masks, divided into a training subset with anomaly-free samples and a validation (test) subset with normal and anomalous samples.

Also known as
Metal Parts Defect Detection Dataset, Metal Parts Defect Detection
Domain
manufacturing inspection
License
CC BY-NC-SA 4.0 (LICENSE file added to the repository in July 2024)
Test items from
2021-09-12
Evidence
“2021-09-12T17:06:28Z”
api.github.com · code · commit 'Add download link' (26362eb), author date · retrieved 2026-10-15
Homepage
github.com

Exposure by model

BenchmarkModelLevelReason
Industrial anomaly detection AA-CLIP, trained on MVTec AD (for VisA results) Unknown unknown for its base model OpenAI CLIP: training data not disclosed in enough detail
Industrial anomaly detection AA-CLIP, trained on VisA Unknown unknown for its base model OpenAI CLIP: training data not disclosed in enough detail
Industrial anomaly detection AdaCLIP, trained on all 14 datasets (demo model) Test data seen in fine-tuning data: its test split was used for training
Industrial anomaly detection AdaCLIP, trained on MVTec AD + ColonDB Unknown unknown for its base model OpenAI CLIP: training data not disclosed in enough detail
Industrial anomaly detection AdaCLIP, trained on VisA + ClinicDB Unknown unknown for its base model OpenAI CLIP: training data not disclosed in enough detail
Industrial anomaly detection AdaptCLIP, trained on MVTec AD Unknown unknown for its base model OpenAI CLIP: training data not disclosed in enough detail
Industrial anomaly detection AdaptCLIP, trained on VisA Unknown unknown for its base model OpenAI CLIP: training data not disclosed in enough detail
Industrial anomaly detection Anomaly-OneVision (Anomaly-OV) Unknown unknown for its base model LLaVA-OneVision: unknown for its base model SigLIP: training data not disclosed in enough detail
Industrial anomaly detection AnomalyCLIP, trained on MVTec AD Unknown unknown for its base model OpenAI CLIP: training data not disclosed in enough detail
Industrial anomaly detection AnomalyCLIP, trained on VisA Unknown unknown for its base model OpenAI CLIP: training data not disclosed in enough detail
Industrial anomaly detection AnomalyDINO Unknown unknown for its base model DINOv2: training data not disclosed in enough detail
Industrial anomaly detection AnomalyGPT, unsupervised on MVTec AD Unknown training data not disclosed in enough detail
Industrial anomaly detection AnomalyGPT, supervised (demo model) Unknown training data not disclosed in enough detail
Industrial anomaly detection AnomalyGPT, unsupervised on VisA Unknown training data not disclosed in enough detail
Industrial anomaly detection APRIL-GAN (VAND 2023), trained on MVTec AD Unknown unknown for its base model OpenAI CLIP: training data not disclosed in enough detail
Industrial anomaly detection APRIL-GAN (VAND 2023), trained on VisA Unknown unknown for its base model OpenAI CLIP: training data not disclosed in enough detail
Industrial anomaly detection OpenAI CLIP Unknown training data not disclosed in enough detail
Industrial anomaly detection DINOv2 Unknown training data not disclosed in enough detail
Industrial anomaly detection DINOv3 (web / LVD-1689M models) Unknown training data not disclosed in enough detail
Industrial anomaly detection FiLo, trained on MVTec AD Unknown unknown for its base model OpenAI CLIP: training data not disclosed in enough detail
Industrial anomaly detection FiLo, trained on VisA Unknown unknown for its base model OpenAI CLIP: training data not disclosed in enough detail
Industrial anomaly detection WideResNet-50-2 (supervised ImageNet-1k, torchvision) Not seen absent from the complete list of training data
Industrial anomaly detection InCTRL, trained on MVTec AD Unknown unknown for its base model OpenCLIP (LAION-400M checkpoints): training data not disclosed in enough detail
Industrial anomaly detection InCTRL, trained on VisA Unknown unknown for its base model OpenCLIP (LAION-400M checkpoints): training data not disclosed in enough detail
Industrial anomaly detection MetaUAS Unknown training data not disclosed in enough detail
Industrial anomaly detection MuSc Unknown unknown for its base model OpenAI CLIP: training data not disclosed in enough detail
Industrial anomaly detection OpenCLIP (LAION-2B checkpoints) Unknown training data not disclosed in enough detail
Industrial anomaly detection OpenCLIP (LAION-400M checkpoints) Unknown training data not disclosed in enough detail
Industrial anomaly detection Segment Any Anomaly (SAA / SAA+) Unknown unknown for its base model Segment Anything Model (SAM): training data not disclosed in enough detail
Industrial anomaly detection SAM 2 / SAM 2.1 Unknown training data not disclosed in enough detail
Industrial anomaly detection Segment Anything Model (SAM) Unknown training data not disclosed in enough detail
Industrial anomaly detection SigLIP 2 Unknown training data not disclosed in enough detail
Industrial anomaly detection SigLIP Unknown training data not disclosed in enough detail
Industrial anomaly detection UniVAD Unknown unknown for its base model OpenAI CLIP: training data not disclosed in enough detail
Industrial anomaly detection VCP-CLIP, trained on MVTec-AD Unknown unknown for its base model OpenAI CLIP: training data not disclosed in enough detail
Industrial anomaly detection VCP-CLIP, trained on VisA Unknown unknown for its base model OpenAI CLIP: training data not disclosed in enough detail
Industrial anomaly detection WinCLIP / WinCLIP+ Unknown unknown for its base model OpenCLIP (LAION-400M checkpoints): training data not disclosed in enough detail

Notes

published_from = date of the GitHub commit that added the download link (repository created 2021-09-12); whether the repository was public that day is not documented. The paper (ICUMT 2021, DOI 10.1109/ICUMT54235.2021.9631567) is recorded as published 13.12.2021. Data is downloaded from a university SharePoint/OneDrive folder. The paper itself (IEEE Xplore) was not fetched, so per-category counts are not given.

Sources