{"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/parse-cfg","entry":"parse_cfg","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":19,"n_papers_ran":2,"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":14,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":20,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":1,"unverified":12},"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":"2410.08751","paper":"/paper/zero-shot-offline-imitation-learning-via","title":"Zero-Shot Offline Imitation Learning via Optimal Transport","date":"2024-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"martius-lab/zilot","path":"zilot/parse.py","file_url":"https://github.com/martius-lab/zilot/blob/HEAD/zilot/parse.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2c5481376eb91faa","mcp_get_code":{"code_sha256":"2c5481376eb91faa"}},{"arxiv_id":"2406.09509","paper":"/paper/cleandiffuser-an-easy-to-use-modularized","title":"CleanDiffuser: An Easy-to-use Modularized Library for Diffusion Models in Decision Making","date":"2024-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Josh00-Lu/DiffusionVeteran","path":"pipelines/utils.py","file_url":"https://github.com/Josh00-Lu/DiffusionVeteran/blob/HEAD/pipelines/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b6b6a836ac8227fc","mcp_get_code":{"code_sha256":"b6b6a836ac8227fc"}},{"arxiv_id":"2405.03379","paper":"/paper/reverse-forward-curriculum-learning-for","title":"Reverse Forward Curriculum Learning for Extreme Sample and Demonstration Efficiency in Reinforcement Learning","date":"2024-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stonet2000/rfcl","path":"rfcl/utils/parse.py","file_url":"https://github.com/stonet2000/rfcl/blob/HEAD/rfcl/utils/parse.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ed51992b2a581ba6","mcp_get_code":{"code_sha256":"ed51992b2a581ba6"}},{"arxiv_id":"2212.05698","paper":"/paper/modem-accelerating-visual-model-based","title":"MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations","date":"2022-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/modem","path":"cfg_parse.py","file_url":"https://github.com/facebookresearch/modem/blob/HEAD/cfg_parse.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"a28e11143461d211","mcp_get_code":{"code_sha256":"a28e11143461d211"}},{"arxiv_id":"2205.00908","paper":"/paper/memseg-a-semi-supervised-method-for-image","title":"MemSeg: A semi-supervised method for image surface defect detection using differences and commonalities","date":"2022-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ntkhoa95/MemSeg-Defect-Detection","path":"conversion.py","file_url":"https://github.com/ntkhoa95/MemSeg-Defect-Detection/blob/HEAD/conversion.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7e4d7de3fdbdb1bc","mcp_get_code":{"code_sha256":"7e4d7de3fdbdb1bc"}},{"arxiv_id":"2203.03373","paper":"/paper/adversarial-texture-for-fooling-person","title":"Adversarial Texture for Fooling Person Detectors in the Physical World","date":"2022-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WhoTHU/Adversarial_Texture","path":"yolo2/cfg.py","file_url":"https://github.com/WhoTHU/Adversarial_Texture/blob/HEAD/yolo2/cfg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"93c283a4f6e6c40f","mcp_get_code":{"code_sha256":"93c283a4f6e6c40f"}},{"arxiv_id":"2010.05501","paper":"/paper/bipointnet-binary-neural-network-for-point-2","title":"BiPointNet: Binary Neural Network for Point Clouds","date":"2020-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"htqin/BiPointNet","path":"utils/utils.py","file_url":"https://github.com/htqin/BiPointNet/blob/HEAD/utils/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"380c68b7dce6f8fa","mcp_get_code":{"code_sha256":"380c68b7dce6f8fa"}},{"arxiv_id":"2004.10934","paper":"/paper/yolov4-optimal-speed-and-accuracy-of-object","title":"YOLOv4: Optimal Speed and Accuracy of Object Detection","date":"2020-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"UtkarshKharche29/Automatic-Number-Plate-Recognition","path":"tool/config.py","file_url":"https://github.com/UtkarshKharche29/Automatic-Number-Plate-Recognition/blob/HEAD/tool/config.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"93c283a4f6e6c40f","mcp_get_code":{"code_sha256":"93c283a4f6e6c40f"}},{"arxiv_id":"2004.10934","paper":"/paper/yolov4-optimal-speed-and-accuracy-of-object","title":"YOLOv4: Optimal Speed and Accuracy of Object Detection","date":"2020-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hhk7734/tensorflow-yolov4","path":"py_src/yolov4/common/parser.py","file_url":"https://github.com/hhk7734/tensorflow-yolov4/blob/HEAD/py_src/yolov4/common/parser.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"70d71d1178ac1ab8","mcp_get_code":{"code_sha256":"70d71d1178ac1ab8"}},{"arxiv_id":"1910.13321","paper":"/paper/191013321","title":"Semantic Object Accuracy for Generative Text-to-Image Synthesis","date":"2019-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tohinz/semantic-object-accuracy-for-generative-text-to-image-synthesis","path":"SOA/darknet.py","file_url":"https://github.com/tohinz/semantic-object-accuracy-for-generative-text-to-image-synthesis/blob/HEAD/SOA/darknet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4587b69d57d6511b","mcp_get_code":{"code_sha256":"4587b69d57d6511b"}},{"arxiv_id":"1910.00068","paper":"/paper/adversarial-patches-exploiting-contextual","title":"Role of Spatial Context in Adversarial Robustness for Object Detection","date":"2019-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"UMBCvision/Contextual-Adversarial-Patches","path":"cfg.py","file_url":"https://github.com/UMBCvision/Contextual-Adversarial-Patches/blob/HEAD/cfg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"93c283a4f6e6c40f","mcp_get_code":{"code_sha256":"93c283a4f6e6c40f"}},{"arxiv_id":"1904.08653","paper":"/paper/fooling-automated-surveillance-cameras","title":"Fooling automated surveillance cameras: adversarial patches to attack person detection","date":"2019-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Zhang-Jack/adversarial_yolo2","path":"cfg.py","file_url":"https://github.com/Zhang-Jack/adversarial_yolo2/blob/HEAD/cfg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"93c283a4f6e6c40f","mcp_get_code":{"code_sha256":"93c283a4f6e6c40f"}},{"arxiv_id":"1812.02541","paper":"/paper/segmentation-driven-6d-object-pose-estimation","title":"Segmentation-driven 6D Object Pose Estimation","date":"2018-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AP-EPFL/DA-segmentation-driven-pose","path":"cfg.py","file_url":"https://github.com/AP-EPFL/DA-segmentation-driven-pose/blob/HEAD/cfg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b5bfd6725600f300","mcp_get_code":{"code_sha256":"b5bfd6725600f300"}},{"arxiv_id":"1812.01387","paper":"/paper/estimating-6d-pose-from-localizing-designated","title":"Estimating 6D Pose From Localizing Designated Surface Keypoints","date":"2018-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sjtuytc/betapose","path":"3_6Dpose_estimator/yolo/darknet.py","file_url":"https://github.com/sjtuytc/betapose/blob/HEAD/3_6Dpose_estimator/yolo/darknet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4587b69d57d6511b","mcp_get_code":{"code_sha256":"4587b69d57d6511b"}},{"arxiv_id":"1804.02767","paper":"/paper/yolov3-an-incremental-improvement","title":"YOLOv3: An Incremental Improvement","date":"2018-04-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GunjanChourasia/custom_detector_yolov3","path":"darknet.py","file_url":"https://github.com/GunjanChourasia/custom_detector_yolov3/blob/HEAD/darknet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4587b69d57d6511b","mcp_get_code":{"code_sha256":"4587b69d57d6511b"}},{"arxiv_id":"1506.02640","paper":"/paper/you-only-look-once-unified-real-time-object","title":"You Only Look Once: Unified, Real-Time Object Detection","date":"2015-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"3epochs/you-only-look-once","path":"darknet.py","file_url":"https://github.com/3epochs/you-only-look-once/blob/HEAD/darknet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"18a29e8e5e9a500b","mcp_get_code":{"code_sha256":"18a29e8e5e9a500b"}},{"arxiv_id":"aaai_28123","paper":null,"title":"arXiv:aaai_28123","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"sugar-fly/VSFormer","path":"models/vanilla_transformer.py","file_url":"https://github.com/sugar-fly/VSFormer/blob/HEAD/models/vanilla_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"135c1dcf77712d97","mcp_get_code":{"code_sha256":"135c1dcf77712d97"}},{"arxiv_id":"Liu_UNOPose_Unseen_Object_Pose_Estimation_with_an_Unposed_RGB-D_Reference_CVPR_2025_paper","paper":null,"title":"arXiv:Liu_UNOPose_Unseen_Object_Pose_Estimation_with_an_Unposed_RGB-D_Reference_CVPR_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"shanice-l/UNOPose","path":"core/unopose/model/transformer.py","file_url":"https://github.com/shanice-l/UNOPose/blob/HEAD/core/unopose/model/transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"135c1dcf77712d97","mcp_get_code":{"code_sha256":"135c1dcf77712d97"}},{"arxiv_id":"Lin_HiPose_Hierarchical_Binary_Surface_Encoding_and_Correspondence_Pruning_for_RGB-D_CVPR_2024_paper","paper":null,"title":"arXiv:Lin_HiPose_Hierarchical_Binary_Surface_Encoding_and_Correspondence_Pruning_for_RGB-D_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"lyltc1/HiPose","path":"hipose/config_parser.py","file_url":"https://github.com/lyltc1/HiPose/blob/HEAD/hipose/config_parser.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2fef6610b1ad3911","mcp_get_code":{"code_sha256":"2fef6610b1ad3911"}},{"arxiv_id":"2024.acl-demos.24","paper":null,"title":"arXiv:2024.acl-demos.24","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"mhulden/pyfoma","path":"src/pyfoma/cfg.py","file_url":"https://github.com/mhulden/pyfoma/blob/HEAD/src/pyfoma/cfg.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"019f21efeabe338f","mcp_get_code":{"code_sha256":"019f21efeabe338f"}}]}