{"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/efficientnet","entry":"efficientnet","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":8,"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":8,"n_samples_ran":1,"n_samples_fingerprinted":1,"n_places":11,"n_places_pointer_only":4,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":0,"unverified":7},"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":"2411.01981","paper":"/paper/typicalness-aware-learning-for-failure","title":"Typicalness-Aware Learning for Failure Detection","date":"2024-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liuyijungoon/TAL","path":"model/efficientnet.py","file_url":"https://github.com/liuyijungoon/TAL/blob/HEAD/model/efficientnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4fa7bc5b18c28b84","mcp_get_code":{"code_sha256":"4fa7bc5b18c28b84"}},{"arxiv_id":"2403.02886","paper":"/paper/revisiting-confidence-estimation-towards","title":"Revisiting Confidence Estimation: Towards Reliable Failure Prediction","date":"2024-03-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"impression2805/fmfp","path":"model/efficientnet.py","file_url":"https://github.com/impression2805/fmfp/blob/HEAD/model/efficientnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4fa7bc5b18c28b84","mcp_get_code":{"code_sha256":"4fa7bc5b18c28b84"}},{"arxiv_id":"2403.01786","paper":"/paper/exposing-the-deception-uncovering-more","title":"Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection","date":"2024-03-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qingyuliu/exposing-the-deception","path":"models/MI_Net.py","file_url":"https://github.com/qingyuliu/exposing-the-deception/blob/HEAD/models/MI_Net.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":"7c07cd76854d45e9","mcp_get_code":{"code_sha256":"7c07cd76854d45e9"}},{"arxiv_id":"2208.08630","paper":"/paper/unifying-visual-perception-by-dispersible","title":"Unifying Visual Perception by Dispersible Points Learning","date":"2022-08-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Sense-X/UniHead","path":"up/models/backbones/efficientnet.py","file_url":"https://github.com/Sense-X/UniHead/blob/HEAD/up/models/backbones/efficientnet.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":"a088fd1c2c78a12c","mcp_get_code":{"code_sha256":"a088fd1c2c78a12c"}},{"arxiv_id":"2103.02406","paper":"/paper/multi-attentional-deepfake-detection","title":"Multi-attentional Deepfake Detection","date":"2021-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yoctta/multiple-attention","path":"models/MAT.py","file_url":"https://github.com/yoctta/multiple-attention/blob/HEAD/models/MAT.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"257c1c1e61218873","mcp_get_code":{"code_sha256":"257c1c1e61218873"}},{"arxiv_id":"1905.11946","paper":"/paper/efficientnet-rethinking-model-scaling-for","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","date":"2019-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AmirmohammadRostami/KeywordsSpotting-EfficientNet-A0","path":"utils/efficientnet_pytorch/model.py","file_url":"https://github.com/AmirmohammadRostami/KeywordsSpotting-EfficientNet-A0/blob/HEAD/utils/efficientnet_pytorch/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1ec716db2a067c4e","mcp_get_code":{"code_sha256":"1ec716db2a067c4e"}},{"arxiv_id":"1905.11946","paper":"/paper/efficientnet-rethinking-model-scaling-for","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","date":"2019-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lvweiwolf/efficientdet","path":"backbone/efficientnet_builder.py","file_url":"https://github.com/lvweiwolf/efficientdet/blob/HEAD/backbone/efficientnet_builder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"04f39101f07909c0","mcp_get_code":{"code_sha256":"04f39101f07909c0"}},{"arxiv_id":"1905.11946","paper":"/paper/efficientnet-rethinking-model-scaling-for","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","date":"2019-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DableUTeeF/keras-efficientnet","path":"keras_efficientnet/efficientnet_builder.py","file_url":"https://github.com/DableUTeeF/keras-efficientnet/blob/HEAD/keras_efficientnet/efficientnet_builder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dda6eef82d7f7b1d","mcp_get_code":{"code_sha256":"dda6eef82d7f7b1d"}},{"arxiv_id":"1905.11946","paper":"/paper/efficientnet-rethinking-model-scaling-for","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","date":"2019-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ravi02512/efficientdet-keras","path":"backbone/efficientnet_builder.py","file_url":"https://github.com/ravi02512/efficientdet-keras/blob/HEAD/backbone/efficientnet_builder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3428492d04f44096","mcp_get_code":{"code_sha256":"3428492d04f44096"}},{"arxiv_id":"Zhu_RCL_Reliable_Continual_Learning_for_Unified_Failure_Detection_CVPR_2024_paper","paper":null,"title":"arXiv:Zhu_RCL_Reliable_Continual_Learning_for_Unified_Failure_Detection_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Impression2805/RCL","path":"model/efficientnet.py","file_url":"https://github.com/Impression2805/RCL/blob/HEAD/model/efficientnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4fa7bc5b18c28b84","mcp_get_code":{"code_sha256":"4fa7bc5b18c28b84"}},{"arxiv_id":"136850512","paper":null,"title":"arXiv:136850512","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Impression2805/FMFP","path":"model/efficientnet.py","file_url":"https://github.com/Impression2805/FMFP/blob/HEAD/model/efficientnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4fa7bc5b18c28b84","mcp_get_code":{"code_sha256":"4fa7bc5b18c28b84"}}]}