{"url":"/dataset/mvtec-loco-ad","name":"MVTec LOCO AD","full_name":"MVTec Logical Constraints Anomaly Detection","description_markdown":"**MVTec Logical Constraints Anomaly Detection (MVTec LOCO AD)** dataset is intended for the evaluation of unsupervised anomaly localization algorithms. The dataset includes both structural and logical anomalies. It contains 3644 images from five different categories inspired by real-world industrial inspection scenarios. Structural anomalies appear as scratches, dents, or contaminations in the manufactured products. Logical anomalies violate underlying constraints, e.g., a permissible object being present in an invalid location or a required object not being present at all. The dataset also includes pixel-precise ground truth data for each anomalous region.\r\n\r\nSource: [THE MVTEC LOGICAL CONSTRAINTS ANOMALY DETECTION DATASET (MVTEC LOCO AD)](https://www.mvtec.com/company/research/datasets/mvtec-loco)","description_withheld":null,"homepage":"https://www.mvtec.com/company/research/datasets/mvtec-loco","introduced_date":"2022-02-22","introduced_date_note":null,"introduced_by":null,"license":{"name":"CC BY-NC-SA 4.0 license","url":"https://creativecommons.org/licenses/by-nc-sa/4.0/"},"modalities":[],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"Unsupervised Anomaly Detection","url":"/task/unsupervised-anomaly-detection","datasets_with_task":"/datasets/task/unsupervised-anomaly-detection"},{"name":"Semi-supervised Anomaly Detection","url":"/task/semi-supervised-anomaly-detection","datasets_with_task":"/datasets/task/semi-supervised-anomaly-detection"}],"languages":[],"variants":["MVTec LOCO AD"],"data_loaders":[],"num_papers_in_archive":32,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/anomaly-detection-on-mvtec-loco-ad","task":"Anomaly Detection","dataset_variant":"MVTec LOCO AD","rows":40,"metrics":["Avg. Detection AUROC","Detection AUROC (only logical)","Detection AUROC (only structural)","Segmentation AU-sPRO (until FPR 5%)"],"first_row_in_archive_order":{"model":"CSAD","paper":"/paper/csad-unsupervised-component-segmentation-for","metrics":{"Avg. Detection AUROC":"95.3","Detection AUROC (only logical)":"96.7","Detection AUROC (only structural)":"94.0"},"code_links":[{"title":"Tokichan/CSAD","url":"https://github.com/Tokichan/CSAD"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/ladmim-logical-anomaly-detection-with-masked","title":"LADMIM: Logical Anomaly Detection with Masked Image Modeling in Discrete Latent Space","date":"2024-10-14","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/csad-unsupervised-component-segmentation-for","title":"CSAD: Unsupervised Component Segmentation for Logical Anomaly Detection","date":"2024-08-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sam-lad-segment-anything-model-meets-zero","title":"SAM-LAD: Segment Anything Model Meets Zero-Shot Logic Anomaly Detection","date":"2024-06-02","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/puad-frustratingly-simple-method-for-robust-1","title":"PUAD: Frustratingly Simple Method for Robust Anomaly Detection","date":"2024-02-23","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/musc-zero-shot-industrial-anomaly","title":"MuSc: Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images","date":"2024-01-30","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/generating-and-reweighting-dense-contrastive","title":"Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection","date":"2023-12-26","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/few-shot-part-segmentation-reveals","title":"Few Shot Part Segmentation Reveals Compositional Logic for Industrial Anomaly Detection","date":"2023-12-21","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/set-features-for-anomaly-detection","title":"Set Features for Anomaly Detection","date":"2023-11-24","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/contextual-affinity-distillation-for-image","title":"Contextual Affinity Distillation for Image Anomaly Detection","date":"2023-07-06","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/component-aware-anomaly-detection-framework","title":"Component-aware anomaly detection framework for adjustable and logical industrial visual inspection","date":"2023-05-15","rows_on_this_dataset":5,"code_links":1,"syntology":null},{"paper":"/paper/slsg-industrial-image-anomaly-detection-by","title":"SLSG: Industrial Image Anomaly Detection by Learning Better Feature Embeddings and One-Class Classification","date":"2023-04-30","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/visual-anomaly-detection-via-dual-attention","title":"Visual Anomaly Detection via Dual-Attention Transformer and Discriminative Flow","date":"2023-03-31","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/hard-nominal-example-aware-template-mutual","title":"Hard-normal Example-aware Template Mutual Matching for Industrial Anomaly Detection","date":"2023-03-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/simplenet-a-simple-network-for-image-anomaly","title":"SimpleNet: A Simple Network for Image Anomaly Detection and Localization","date":"2023-03-27","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":17,"samples_ran":10,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/efficientad-accurate-visual-anomaly-detection","title":"EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies","date":"2023-03-25","rows_on_this_dataset":2,"code_links":33,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":35,"samples_ran":1,"samples_unverified":34,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-global-local-correspondence-with","title":"Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection","date":"2023-03-10","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/set-features-for-fine-grained-anomaly","title":"Set Features for Fine-grained Anomaly Detection","date":"2023-02-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/template-guided-hierarchical-feature","title":"Template-guided Hierarchical Feature Restoration for Anomaly Detection","date":"2023-01-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/asymmetric-student-teacher-networks-for","title":"Asymmetric Student-Teacher Networks for Industrial Anomaly Detection","date":"2022-10-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dsr-a-dual-subspace-re-projection-network-for","title":"DSR -- A dual subspace re-projection network for surface anomaly detection","date":"2022-08-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/beyond-dents-and-scratches-logical","title":"Beyond Dents and Scratches: Logical Constraints in Unsupervised Anomaly Detection and Localization","date":"2022-02-22","rows_on_this_dataset":3,"code_links":0,"syntology":null},{"paper":"/paper/anomaly-detection-via-reverse-distillation","title":"Anomaly Detection via Reverse Distillation from One-Class Embedding","date":"2022-01-26","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":18,"samples_ran":14,"samples_unverified":4,"pointer_only_for_licence":15,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fastflow-unsupervised-anomaly-detection-and","title":"FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows","date":"2021-11-15","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":0,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/draem-a-discriminatively-trained","title":"DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection","date":"2021-08-17","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":2,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/towards-total-recall-in-industrial-anomaly","title":"Towards Total Recall in Industrial Anomaly Detection","date":"2021-06-15","rows_on_this_dataset":2,"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."}},{"paper":"/paper/sub-image-anomaly-detection-with-deep-pyramid","title":"Sub-Image Anomaly Detection with Deep Pyramid Correspondences","date":"2020-05-05","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-memory-guided-normality-for-anomaly","title":"Learning Memory-guided Normality for Anomaly Detection","date":"2020-03-30","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/uninformed-students-student-teacher-anomaly","title":"Uninformed Students: Student-Teacher Anomaly Detection with Discriminative Latent Embeddings","date":"2019-11-06","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":8,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/f-anogan-fast-unsupervised-anomaly-detection","title":"f-AnoGAN: Fast Unsupervised Anomaly Detection with Generative Adversarial Networks","date":"2019-01-30","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/auto-encoding-variational-bayes","title":"Auto-Encoding Variational Bayes","date":"2013-12-20","rows_on_this_dataset":1,"code_links":144,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":199,"samples_ran":112,"samples_unverified":87,"pointer_only_for_licence":103,"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":11,"samples_harvested":333,"samples_ran":156,"samples_unverified":177,"pointer_only_for_licence":124,"papers_with_no_sample_that_ran":2,"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."}