{"url":"/dataset/goodsad","name":"GoodsAD","full_name":null,"description_markdown":"The GoodsAD dataset contains 6124 images with 6 categories of common supermarket goods. Each category contains multiple goods. All images are acquired with 3000 × 3000 high-resolution. The object locations in the images are not aligned. Most objects are in the center of the images and one image only contains a single object. Most anomalies occupy only a small fraction of image pixels. Both image-level and pixel-level annotations are provided.\r\n\r\nEach image is named with 6 digits, with the first three digits representing the category of the product and the last three representing the serial number. The dataset format is same as MVTec AD.","description_withheld":null,"homepage":"https://github.com/jianzhang96/GoodsAD","introduced_date":"2023-07-11","introduced_date_note":null,"introduced_by":{"paper":"/paper/pku-goodsad-a-supermarket-goods-dataset-for","title":"PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentation","first_author":"Jian Zhang","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Video Anomaly Detection","url":"/task/video-anomaly-detection","datasets_with_task":"/datasets/task/video-anomaly-detection"},{"name":"Anomaly Classification","url":"/task/anomaly-classification","datasets_with_task":"/datasets/task/anomaly-classification"},{"name":"Anomaly Segmentation","url":"/task/anomaly-segmentation","datasets_with_task":"/datasets/task/anomaly-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["GoodsAD"],"data_loaders":[],"num_papers_in_archive":15,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/anomaly-classification-on-goodsad","task":"Anomaly Classification","dataset_variant":"GoodsAD","rows":11,"metrics":["AUPR","AUROC"],"first_row_in_archive_order":{"model":"PatchCore-100%","paper":"/paper/towards-total-recall-in-industrial-anomaly","metrics":{"AUPR":"86.1","AUROC":"85.5"},"code_links":[{"title":"openvinotoolkit/anomalib","url":"https://github.com/openvinotoolkit/anomalib"},{"title":"amazon-science/patchcore-inspection","url":"https://github.com/amazon-science/patchcore-inspection"},{"title":"amazon-research/patchcore-inspection","url":"https://github.com/amazon-research/patchcore-inspection"},{"title":"hcw-00/PatchCore_anomaly_detection","url":"https://github.com/hcw-00/PatchCore_anomaly_detection"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/official/cv/patchcore"},{"title":"rvorias/ind_knn_ad","url":"https://github.com/rvorias/ind_knn_ad"},{"title":"OpenAOI/anodet","url":"https://github.com/OpenAOI/anodet"},{"title":"Burf/tfdetection","url":"https://github.com/Burf/tfdetection"},{"title":"tbcvContributor/DeepHawkeye","url":"https://github.com/tbcvContributor/DeepHawkeye"},{"title":"Ultranity/Anomaly.Paddle","url":"https://github.com/Ultranity/Anomaly.Paddle"},{"title":"tiskw/patchcore-ad","url":"https://github.com/tiskw/patchcore-ad"},{"title":"any-tech/PatchCore-ex","url":"https://github.com/any-tech/PatchCore-ex"},{"title":"taikiinoue45/PatchCore","url":"https://github.com/taikiinoue45/PatchCore"},{"title":"JoegameZhou/PatchCore","url":"https://github.com/JoegameZhou/PatchCore"},{"title":"captainfffsama/pathcore","url":"https://github.com/captainfffsama/pathcore"},{"title":"yangyucheng000/patchcore","url":"https://github.com/yangyucheng000/patchcore"},{"title":"totoroKalic/patchcore-mindspore","url":"https://github.com/totoroKalic/patchcore-mindspore"},{"title":"2023-MindSpore-1/ms-code-4","url":"https://github.com/2023-MindSpore-1/ms-code-4/tree/main/PatchCore"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/minimaxad-a-lightweight-autoencoder-for","title":"MiniMaxAD: A Lightweight Autoencoder for Feature-Rich Anomaly Detection","date":"2024-05-16","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/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/self-supervised-out-of-distribution-detection-1","title":"Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization","date":"2021-09-30","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":2,"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/cflow-ad-real-time-unsupervised-anomaly","title":"CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows","date":"2021-07-27","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":3,"samples_unverified":9,"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/cutpaste-self-supervised-learning-for-anomaly","title":"CutPaste: Self-Supervised Learning for Anomaly Detection and Localization","date":"2021-04-08","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":2,"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/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}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":8,"samples_harvested":104,"samples_ran":39,"samples_unverified":65,"pointer_only_for_licence":17,"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."}