{"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":"/code/py-cpu-nms","entry":"py_cpu_nms","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":13,"n_papers_ran":9,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":4,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":14,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":0,"unverified":3},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2411.02537","paper":"/paper/inquire-a-natural-world-text-to-image","title":"INQUIRE: A Natural World Text-to-Image Retrieval Benchmark","date":"2024-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"biubug6/Pytorch_Retinaface","path":"utils/nms/py_cpu_nms.py","file_url":"https://github.com/biubug6/Pytorch_Retinaface/blob/HEAD/utils/nms/py_cpu_nms.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5502a789c2da0e5e","mcp_get_code":{"code_sha256":"5502a789c2da0e5e"}},{"arxiv_id":"2302.01392","paper":"/paper/moe-fusion-instance-embedded-mixture-of","title":"Multi-modal Gated Mixture of Local-to-Global Experts for Dynamic Image Fusion","date":"2023-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunym2020/moe-fusion","path":"DOTA_devkit/ResultMerge.py","file_url":"https://github.com/sunym2020/moe-fusion/blob/HEAD/DOTA_devkit/ResultMerge.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5502a789c2da0e5e","mcp_get_code":{"code_sha256":"5502a789c2da0e5e"}},{"arxiv_id":"2005.09973","paper":"/paper/dynamic-refinement-network-for-oriented-and","title":"Dynamic Refinement Network for Oriented and Densely Packed Object Detection","date":"2020-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Anymake/DRN_CVPR2020","path":"angle_nms/angle_soft_nms.py","file_url":"https://github.com/Anymake/DRN_CVPR2020/blob/HEAD/angle_nms/angle_soft_nms.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"GPL-2.0","inline_ok":false,"code_sha256_prefix":"5502a789c2da0e5e","mcp_get_code":{"code_sha256":"5502a789c2da0e5e"}},{"arxiv_id":"1912.04799","paper":"/paper/learning-depth-guided-convolutions-for","title":"Learning Depth-Guided Convolutions for Monocular 3D Object Detection","date":"2019-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dingmyu/D4LCN","path":"lib/nms/py_cpu_nms.py","file_url":"https://github.com/dingmyu/D4LCN/blob/HEAD/lib/nms/py_cpu_nms.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5502a789c2da0e5e","mcp_get_code":{"code_sha256":"5502a789c2da0e5e"}},{"arxiv_id":"1907.06038","paper":"/paper/m3d-rpn-monocular-3d-region-proposal-network","title":"M3D-RPN: Monocular 3D Region Proposal Network for Object Detection","date":"2019-07-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"garrickbrazil/M3D-RPN","path":"lib/nms/py_cpu_nms.py","file_url":"https://github.com/garrickbrazil/M3D-RPN/blob/HEAD/lib/nms/py_cpu_nms.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5502a789c2da0e5e","mcp_get_code":{"code_sha256":"5502a789c2da0e5e"}},{"arxiv_id":"1905.02244","paper":"/paper/searching-for-mobilenetv3","title":"Searching for MobileNetV3","date":"2019-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"emilianavt/OpenSeeFace","path":"retinaface.py","file_url":"https://github.com/emilianavt/OpenSeeFace/blob/HEAD/retinaface.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"1cbae9505b4428d7","mcp_get_code":{"code_sha256":"1cbae9505b4428d7"}},{"arxiv_id":"1905.00641","paper":"/paper/190500641","title":"RetinaFace: Single-stage Dense Face Localisation in the Wild","date":"2019-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"2275519762/RetinaFace_Pytorch","path":"utils/nms/py_cpu_nms.py","file_url":"https://github.com/2275519762/RetinaFace_Pytorch/blob/HEAD/utils/nms/py_cpu_nms.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5502a789c2da0e5e","mcp_get_code":{"code_sha256":"5502a789c2da0e5e"}},{"arxiv_id":"1905.00641","paper":"/paper/190500641","title":"RetinaFace: Single-stage Dense Face Localisation in the Wild","date":"2019-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hhj1897/face_detection","path":"ibug/face_detection/retina_face/py_cpu_nms.py","file_url":"https://github.com/hhj1897/face_detection/blob/HEAD/ibug/face_detection/retina_face/py_cpu_nms.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"772670c1695ac7b9","mcp_get_code":{"code_sha256":"772670c1695ac7b9"}},{"arxiv_id":"1812.00155","paper":"/paper/learning-roi-transformer-for-detecting","title":"Learning RoI Transformer for Detecting Oriented Objects in Aerial Images","date":"2018-12-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dingjiansw101/RoITransformer_DOTA","path":"dota_kit/ResultMerge.py","file_url":"https://github.com/dingjiansw101/RoITransformer_DOTA/blob/HEAD/dota_kit/ResultMerge.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"5502a789c2da0e5e","mcp_get_code":{"code_sha256":"5502a789c2da0e5e"}},{"arxiv_id":"1804.05113","paper":"/paper/multilevel-language-and-vision-integration","title":"Multilevel Language and Vision Integration for Text-to-Clip Retrieval","date":"2018-04-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VisionLearningGroup/Text-to-Clip_Retrieval","path":"lib/nms/py_cpu_nms.py","file_url":"https://github.com/VisionLearningGroup/Text-to-Clip_Retrieval/blob/HEAD/lib/nms/py_cpu_nms.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0087afa9a6463790","mcp_get_code":{"code_sha256":"0087afa9a6463790"}},{"arxiv_id":"1802.10250","paper":"/paper/joint-event-detection-and-description-in","title":"Joint Event Detection and Description in Continuous Video Streams","date":"2018-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VisionLearningGroup/JEDDi-Net","path":"lib/nms/py_cpu_nms.py","file_url":"https://github.com/VisionLearningGroup/JEDDi-Net/blob/HEAD/lib/nms/py_cpu_nms.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0087afa9a6463790","mcp_get_code":{"code_sha256":"0087afa9a6463790"}},{"arxiv_id":"1703.07814","paper":"/paper/r-c3d-region-convolutional-3d-network-for","title":"R-C3D: Region Convolutional 3D Network for Temporal Activity Detection","date":"2017-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VisionLearningGroup/R-C3D","path":"lib/nms/py_cpu_nms.py","file_url":"https://github.com/VisionLearningGroup/R-C3D/blob/HEAD/lib/nms/py_cpu_nms.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0087afa9a6463790","mcp_get_code":{"code_sha256":"0087afa9a6463790"}},{"arxiv_id":"1506.01497","paper":"/paper/faster-r-cnn-towards-real-time-object","title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","date":"2015-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"playerkk/face-py-faster-rcnn","path":"lib/nms/py_cpu_nms.py","file_url":"https://github.com/playerkk/face-py-faster-rcnn/blob/HEAD/lib/nms/py_cpu_nms.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5502a789c2da0e5e","mcp_get_code":{"code_sha256":"5502a789c2da0e5e"}},{"arxiv_id":"Xu_Rethinking_Boundary_Discontinuity_Problem_for_Oriented_Object_Detection_CVPR_2024_paper","paper":null,"title":"arXiv:Xu_Rethinking_Boundary_Discontinuity_Problem_for_Oriented_Object_Detection_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"hangxu-cv/cvpr24acm","path":"DOTA_devkit/ResultMerge.py","file_url":"https://github.com/hangxu-cv/cvpr24acm/blob/HEAD/DOTA_devkit/ResultMerge.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5502a789c2da0e5e","mcp_get_code":{"code_sha256":"5502a789c2da0e5e"}}]}