{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deepfashion2-a-versatile-benchmark-for","title":"DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images","arxiv_id":"1901.07973","date":"2019-01-23","proceeding":"CVPR 2019 6","authors":["Yuying Ge","Ruimao Zhang","Lingyun Wu","Xiaogang Wang","Xiaoou Tang","Ping Luo"],"abstract":"Understanding fashion images has been advanced by benchmarks with rich\nannotations such as DeepFashion, whose labels include clothing categories,\nlandmarks, and consumer-commercial image pairs. However, DeepFashion has\nnonnegligible issues such as single clothing-item per image, sparse landmarks\n(4~8 only), and no per-pixel masks, making it had significant gap from\nreal-world scenarios. We fill in the gap by presenting DeepFashion2 to address\nthese issues. It is a versatile benchmark of four tasks including clothes\ndetection, pose estimation, segmentation, and retrieval. It has 801K clothing\nitems where each item has rich annotations such as style, scale, viewpoint,\nocclusion, bounding box, dense landmarks and masks. There are also 873K\nCommercial-Consumer clothes pairs. A strong baseline is proposed, called Match\nR-CNN, which builds upon Mask R-CNN to solve the above four tasks in an\nend-to-end manner. Extensive evaluations are conducted with different\ncriterions in DeepFashion2.","url_abs":"http://arxiv.org/abs/1901.07973v1","url_pdf":"http://arxiv.org/pdf/1901.07973v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deepfashion2-a-versatile-benchmark-for","repo_url":"https://github.com/switchablenorms/DeepFashion2","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"deepfashion2-a-versatile-benchmark-for","repo_url":"https://github.com/AlberetOZ/WondeRobe_Clothes_test","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"deepfashion2-a-versatile-benchmark-for","repo_url":"https://github.com/SCP-173-cool/match_rcnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"deepfashion2-a-versatile-benchmark-for","repo_url":"https://github.com/ScaDS/Match-R-CNN-Repoduction","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"deepfashion2-a-versatile-benchmark-for","repo_url":"https://github.com/ccc013/DeepLearning_Notes","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"retrieval","task_name":"Retrieval"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"mask-r-cnn","method_name":"Mask R-CNN"},{"method_slug":"rpn","method_name":"RPN"},{"method_slug":"roi-align","method_name":"RoIAlign"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[{"slug":"deepfashion2","name":"DeepFashion2","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1901.07973","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1901.07973"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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