{"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/batch-intersection-union","entry":"batch_intersection_union","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":10,"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":10,"n_places_pointer_only":5,"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":"2609.00853","paper":"/paper/arxiv-2609-00853","title":"ADGNet: Asymmetric Dual-text Guided Network for Infrared Small Target Detection","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"iLearn-Lab/MM26-ADGNet","path":"metrics.py","file_url":"https://github.com/iLearn-Lab/MM26-ADGNet/blob/HEAD/metrics.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":"6237fa9642219064","mcp_get_code":{"code_sha256":"6237fa9642219064"}},{"arxiv_id":"2609.00666","paper":"/paper/arxiv-2609-00666","title":"DGNet: Dual-knowledge Guided Network for Infrared Small Target Detection","date":null,"month_inferred_from_arxiv_id":"2026-09","title_source":"syntology","repo":"iLearn-Lab/MM26-DGNet","path":"metrics.py","file_url":"https://github.com/iLearn-Lab/MM26-DGNet/blob/HEAD/metrics.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":"6237fa9642219064","mcp_get_code":{"code_sha256":"6237fa9642219064"}},{"arxiv_id":"2505.14556","paper":"/paper/dynadiff-single-stage-decoding-of-images-from","title":"Dynadiff: Single-stage Decoding of Images from Continuously Evolving fMRI","date":"2025-05-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/dynadiff","path":"metrics/mIOU/eval_miou.py","file_url":"https://github.com/facebookresearch/dynadiff/blob/HEAD/metrics/mIOU/eval_miou.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"d09bf6c7c12f65f3","mcp_get_code":{"code_sha256":"d09bf6c7c12f65f3"}},{"arxiv_id":"2409.12448","paper":"/paper/infrared-small-target-detection-in-satellite","title":"Infrared Small Target Detection in Satellite Videos: A New Dataset and A Novel Recurrent Feature Refinement Framework","date":"2024-09-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xinyiying/rfr","path":"codes/metrics.py","file_url":"https://github.com/xinyiying/rfr/blob/HEAD/codes/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6237fa9642219064","mcp_get_code":{"code_sha256":"6237fa9642219064"}},{"arxiv_id":"2403.19366","paper":"/paper/infrared-small-target-detection-with-scale","title":"Infrared Small Target Detection with Scale and Location Sensitivity","date":"2024-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ying-fu/mshnet","path":"utils/metric.py","file_url":"https://github.com/ying-fu/mshnet/blob/HEAD/utils/metric.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"33361edfc92a6db7","mcp_get_code":{"code_sha256":"33361edfc92a6db7"}},{"arxiv_id":"2401.15583","paper":"/paper/sctransnet-spatial-channel-cross-transformer","title":"SCTransNet: Spatial-channel Cross Transformer Network for Infrared Small Target Detection","date":"2024-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xdfai/sctransnet","path":"metrics.py","file_url":"https://github.com/xdfai/sctransnet/blob/HEAD/metrics.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6237fa9642219064","mcp_get_code":{"code_sha256":"6237fa9642219064"}},{"arxiv_id":"2310.01680","paper":null,"title":"arXiv:2310.01680","date":null,"month_inferred_from_arxiv_id":"2023-10","title_source":null,"repo":"zshyang/kaf","path":"metrics.py","file_url":"https://github.com/zshyang/kaf/blob/HEAD/metrics.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"226c1f1c175e7261","mcp_get_code":{"code_sha256":"226c1f1c175e7261"}},{"arxiv_id":"2106.00487","paper":"/paper/dense-nested-attention-network-for-infrared","title":"Dense Nested Attention Network for Infrared Small Target Detection","date":"2021-06-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YeRen123455/Infrared-Small-Target-Detection","path":"model/metric.py","file_url":"https://github.com/YeRen123455/Infrared-Small-Target-Detection/blob/HEAD/model/metric.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"33361edfc92a6db7","mcp_get_code":{"code_sha256":"33361edfc92a6db7"}},{"arxiv_id":"1902.04502","paper":"/paper/fast-scnn-fast-semantic-segmentation-network","title":"Fast-SCNN: Fast Semantic Segmentation Network","date":"2019-02-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Tramac/Fast-SCNN-pytorch","path":"utils/metric.py","file_url":"https://github.com/Tramac/Fast-SCNN-pytorch/blob/HEAD/utils/metric.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":"b0028c3b51c18eda","mcp_get_code":{"code_sha256":"b0028c3b51c18eda"}},{"arxiv_id":"1612.01105","paper":"/paper/pyramid-scene-parsing-network","title":"Pyramid Scene Parsing Network","date":"2016-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YininKorea/Contour-aware-equipotential-learning","path":"utils/Evaluator.py","file_url":"https://github.com/YininKorea/Contour-aware-equipotential-learning/blob/HEAD/utils/Evaluator.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a593c508c5bef220","mcp_get_code":{"code_sha256":"a593c508c5bef220"}}]}