{"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/get-anchors","entry":"get_anchors","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":6,"n_papers_ran":1,"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":6,"n_samples_ran":1,"n_samples_fingerprinted":0,"n_places":7,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":1,"unverified":5},"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":"2401.01887","paper":"/paper/leap-vo-long-term-effective-any-point","title":"LEAP-VO: Long-term Effective Any Point Tracking for Visual Odometry","date":"2024-01-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wrchen530/leapvo","path":"main/leap/core/anchor_sampler.py","file_url":"https://github.com/wrchen530/leapvo/blob/HEAD/main/leap/core/anchor_sampler.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"46bc85ee605f6274","mcp_get_code":{"code_sha256":"46bc85ee605f6274"}},{"arxiv_id":"2112.08088","paper":"/paper/image-adaptive-yolo-for-object-detection-in","title":"Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions","date":"2021-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wenyyu/Image-Adaptive-YOLO","path":"core/utils.py","file_url":"https://github.com/wenyyu/Image-Adaptive-YOLO/blob/HEAD/core/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":"eb22d53f46cdff31","mcp_get_code":{"code_sha256":"eb22d53f46cdff31"}},{"arxiv_id":"2008.02093","paper":"/paper/point-proposal-network-accelerating-point","title":"Point Proposal Network: Accelerating Point Source Detection Through Deep Learning","date":"2020-08-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tilleyd/point-proposal-net","path":"ppn/_data_labeling.py","file_url":"https://github.com/tilleyd/point-proposal-net/blob/HEAD/ppn/_data_labeling.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c18af27725d9e822","mcp_get_code":{"code_sha256":"c18af27725d9e822"}},{"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":"michhar/azureml-keras-yolov3-custom","path":"project/train_azureml.py","file_url":"https://github.com/michhar/azureml-keras-yolov3-custom/blob/HEAD/project/train_azureml.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ce2b160dd57455ac","mcp_get_code":{"code_sha256":"ce2b160dd57455ac"}},{"arxiv_id":"1912.12147","paper":"/paper/cooperative-perception-for-3d-object","title":"Cooperative Perception for 3D Object Detection in Driving Scenarios using Infrastructure Sensors","date":"2019-12-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eduardohenriquearnold/coop-3dod-infra","path":"lib/functions/anchor_helper.py","file_url":"https://github.com/eduardohenriquearnold/coop-3dod-infra/blob/HEAD/lib/functions/anchor_helper.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":"9037dfb9aa7b9e43","mcp_get_code":{"code_sha256":"9037dfb9aa7b9e43"}},{"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":"YunYang1994/tensorflow-yolov3","path":"core/utils.py","file_url":"https://github.com/YunYang1994/tensorflow-yolov3/blob/HEAD/core/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"eb22d53f46cdff31","mcp_get_code":{"code_sha256":"eb22d53f46cdff31"}},{"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":"qqwweee/keras-yolo3","path":"train_bottleneck.py","file_url":"https://github.com/qqwweee/keras-yolo3/blob/HEAD/train_bottleneck.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d9196437ddef4b60","mcp_get_code":{"code_sha256":"d9196437ddef4b60"}}]}