{"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/load-url","entry":"load_url","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":9,"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":6,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":11,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":1,"unverified":4},"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":"2601.02091","paper":"/paper/arxiv-2601-02091","title":"MCD-Net: A Lightweight Deep Learning Baseline for Optical-Only Moraine Segmentation","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"Lyra-alpha/MCD-Net","path":"nets/deeplabv3_plus.py","file_url":"https://github.com/Lyra-alpha/MCD-Net/blob/HEAD/nets/deeplabv3_plus.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0896d8636b35a30b","mcp_get_code":{"code_sha256":"0896d8636b35a30b"}},{"arxiv_id":"2507.01630","paper":null,"title":"arXiv:2507.01630","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"YuxiaoWang-AI/P3HOT","path":"hot/models/utils.py","file_url":"https://github.com/YuxiaoWang-AI/P3HOT/blob/HEAD/hot/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d087e320591e6ba7","mcp_get_code":{"code_sha256":"d087e320591e6ba7"}},{"arxiv_id":"2407.07311","paper":"/paper/vitime-a-visual-intelligence-based-foundation","title":"ViTime: A Visual Intelligence-Based Foundation Model for Time Series Forecasting","date":"2024-07-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ikeyang/vitime","path":"model/mobilenetv2.py","file_url":"https://github.com/ikeyang/vitime/blob/HEAD/model/mobilenetv2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b5c8e430bdc8e4ef","mcp_get_code":{"code_sha256":"b5c8e430bdc8e4ef"}},{"arxiv_id":"2203.15207","paper":"/paper/generalizing-few-shot-nas-with-gradient-1","title":"Generalizing Few-Shot NAS with Gradient Matching","date":"2022-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"skhu101/GM-NAS","path":"Proxylessnas-GM/proxyless_nas/utils.py","file_url":"https://github.com/skhu101/GM-NAS/blob/HEAD/Proxylessnas-GM/proxyless_nas/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a7f652bd280d580e","mcp_get_code":{"code_sha256":"a7f652bd280d580e"}},{"arxiv_id":"1908.07919","paper":"/paper/190807919","title":"Deep High-Resolution Representation Learning for Visual Recognition","date":"2019-08-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CSAILVision/semantic-segmentation-pytorch","path":"mit_semseg/models/utils.py","file_url":"https://github.com/CSAILVision/semantic-segmentation-pytorch/blob/HEAD/mit_semseg/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"d087e320591e6ba7","mcp_get_code":{"code_sha256":"d087e320591e6ba7"}},{"arxiv_id":"1904.04514","paper":"/paper/high-resolution-representations-for-labeling","title":"High-Resolution Representations for Labeling Pixels and Regions","date":"2019-04-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kukby/Mish-semantic-segmentation-pytorch","path":"models/utils.py","file_url":"https://github.com/kukby/Mish-semantic-segmentation-pytorch/blob/HEAD/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"d087e320591e6ba7","mcp_get_code":{"code_sha256":"d087e320591e6ba7"}},{"arxiv_id":"1812.00332","paper":"/paper/proxylessnas-direct-neural-architecture","title":"ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware","date":"2018-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MIT-HAN-LAB/ProxylessNAS","path":"proxyless_nas/utils.py","file_url":"https://github.com/MIT-HAN-LAB/ProxylessNAS/blob/HEAD/proxyless_nas/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a7f652bd280d580e","mcp_get_code":{"code_sha256":"a7f652bd280d580e"}},{"arxiv_id":"1812.00332","paper":"/paper/proxylessnas-direct-neural-architecture","title":"ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware","date":"2018-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AhmadQasim/proxylessnas-dense","path":"proxyless_nas/utils.py","file_url":"https://github.com/AhmadQasim/proxylessnas-dense/blob/HEAD/proxyless_nas/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":"2d2825ade23f56b8","mcp_get_code":{"code_sha256":"2d2825ade23f56b8"}},{"arxiv_id":"1807.11590","paper":"/paper/acquisition-of-localization-confidence-for","title":"Acquisition of Localization Confidence for Accurate Object Detection","date":"2018-07-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CSAILVision/unifiedparsing","path":"models/resnext.py","file_url":"https://github.com/CSAILVision/unifiedparsing/blob/HEAD/models/resnext.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a42beb0aba2deb89","mcp_get_code":{"code_sha256":"a42beb0aba2deb89"}},{"arxiv_id":"1807.10221","paper":"/paper/unified-perceptual-parsing-for-scene","title":"Unified Perceptual Parsing for Scene Understanding","date":"2018-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"starkgines/PDI","path":"mit_semseg/models/utils.py","file_url":"https://github.com/starkgines/PDI/blob/HEAD/mit_semseg/models/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"d087e320591e6ba7","mcp_get_code":{"code_sha256":"d087e320591e6ba7"}},{"arxiv_id":"1807.10221","paper":"/paper/unified-perceptual-parsing-for-scene","title":"Unified Perceptual Parsing for Scene Understanding","date":"2018-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SonpKing/semantic-segmentation-pytorch","path":"models/resnext.py","file_url":"https://github.com/SonpKing/semantic-segmentation-pytorch/blob/HEAD/models/resnext.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a42beb0aba2deb89","mcp_get_code":{"code_sha256":"a42beb0aba2deb89"}}]}