{"url":"/dataset/mpdd","name":"MPDD","full_name":"Metal Parts Defect Detection Dataset","description_markdown":"MPDD is a dataset aimed at benchmarking visual defect detection methods in industrial metal parts manufacturing. It consists of more than 1000 images with pixel-precise defect annotation masks. The dataset is divided into the training subset with anomaly-free samples and the validation subset that contains both normal and anomalous samples. The dataset can be downloaded at the following link.","description_withheld":null,"homepage":"https://github.com/stepanje/MPDD","introduced_date":"2021-12-13","introduced_date_note":null,"introduced_by":{"paper":"/paper/deep-learning-based-defect-detection-of-metal","title":"Deep learning-based defect detection of metal parts: evaluating current methods in complex conditions","first_author":"Stepan Jezek","url":null},"license":null,"modalities":[],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"}],"languages":[],"variants":["MPDD"],"data_loaders":[],"num_papers_in_archive":44,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/anomaly-detection-on-mpdd","task":"Anomaly Detection","dataset_variant":"MPDD","rows":16,"metrics":["Detection AUROC","Segmentation AUROC","Segmentation AUPRO"],"first_row_in_archive_order":{"model":"GLASS","paper":"/paper/a-unified-anomaly-synthesis-strategy-with","metrics":{"Detection AUROC":"99.6","Segmentation AUPRO":"98.2","Segmentation AUROC":"99.4"},"code_links":[{"title":"cqylunlun/glass","url":"https://github.com/cqylunlun/glass"},{"title":"septmars/DL","url":"https://github.com/septmars/DL"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/center-aware-residual-anomaly-synthesis-for","title":"Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly Detection","date":"2025-05-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/kanoclip-zero-shot-anomaly-detection-through","title":"KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration","date":"2025-01-07","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/progressive-boundary-guided-anomaly-synthesis","title":"Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection","date":"2024-12-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/revisiting-deep-feature-reconstruction-for","title":"Revisiting Deep Feature Reconstruction for Logical and Structural Industrial Anomaly Detection","date":"2024-10-21","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":0,"samples_unverified":13,"pointer_only_for_licence":13,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dual-modeling-decouple-distillation-for","title":"Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection","date":"2024-08-07","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/adaclip-adapting-clip-with-hybrid-learnable","title":"AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly Detection","date":"2024-07-22","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":28,"samples_ran":19,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/a-unified-anomaly-synthesis-strategy-with","title":"A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization","date":"2024-07-12","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":5,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/glad-towards-better-reconstruction-with","title":"GLAD: Towards Better Reconstruction with Global and Local Adaptive Diffusion Models for Unsupervised Anomaly Detection","date":"2024-06-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":10,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dinomaly-the-less-is-more-philosophy-in-multi","title":"Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection","date":"2024-05-23","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":9,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/realnet-a-feature-selection-network-with","title":"RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly Detection","date":"2024-03-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":9,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/produce-once-utilize-twice-for-anomaly","title":"Produce Once, Utilize Twice for Anomaly Detection","date":"2023-12-20","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/towards-total-online-unsupervised-anomaly","title":"Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision","date":"2023-05-25","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/diffusionad-denoising-diffusion-for-anomaly","title":"DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection","date":"2023-03-15","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/fastrecon-few-shot-industrial-anomaly","title":"FastRecon: Few-shot Industrial Anomaly Detection via Fast Feature Reconstruction","date":"2023-01-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/anomaly-detection-using-score-based","title":"Anomaly Detection using Score-based Perturbation Resilience","date":"2023-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/towards-total-recall-in-industrial-anomaly","title":"Towards Total Recall in Industrial Anomaly Detection","date":"2021-06-15","rows_on_this_dataset":1,"code_links":18,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":36,"samples_ran":5,"samples_unverified":31,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":7,"samples_harvested":120,"samples_ran":57,"samples_unverified":63,"pointer_only_for_licence":13,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}