{"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/conv-3x3-bn","entry":"conv_3x3_bn","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":21,"n_papers_ran":19,"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":10,"n_samples_ran":3,"n_samples_fingerprinted":0,"n_places":27,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"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":"2606.18209","paper":"/paper/arxiv-2606-18209","title":"Rethinking Dataset Distillation for Classification: Do Distilled Sets Outperform Coresets?","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"lin-zhao-resoLve/D3HR","path":"validation/models/mobilenet_v2.py","file_url":"https://github.com/lin-zhao-resoLve/D3HR/blob/HEAD/validation/models/mobilenet_v2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"408e747e0425a594","mcp_get_code":{"code_sha256":"408e747e0425a594"}},{"arxiv_id":"2602.11281","paper":"/paper/arxiv-2602-11281","title":"DeepRed: an architecture for redshift estimation","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"c825b672e6f0b3bc","mcp_get_code":{"code_sha256":"c825b672e6f0b3bc"}},{"arxiv_id":"2507.06482","paper":null,"title":"arXiv:2507.06482","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"hwang52/FedDifRC","path":"model/mobilenetv2.py","file_url":"https://github.com/hwang52/FedDifRC/blob/HEAD/model/mobilenetv2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"408e747e0425a594","mcp_get_code":{"code_sha256":"408e747e0425a594"}},{"arxiv_id":"2507.06482","paper":null,"title":"arXiv:2507.06482","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"hwang52/FedDifRC","path":"model/mobilenetv3.py","file_url":"https://github.com/hwang52/FedDifRC/blob/HEAD/model/mobilenetv3.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5221887077e3fb54","mcp_get_code":{"code_sha256":"5221887077e3fb54"}},{"arxiv_id":"2403.14729","paper":"/paper/auto-train-once-controller-network-guided","title":"Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch","date":"2024-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xidongwu/autotrainonce","path":"imgnet_models/mobilenetv2_custom.py","file_url":"https://github.com/xidongwu/autotrainonce/blob/HEAD/imgnet_models/mobilenetv2_custom.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"408e747e0425a594","mcp_get_code":{"code_sha256":"408e747e0425a594"}},{"arxiv_id":"2311.03747","paper":"/paper/sbcformer-lightweight-network-capable-of-full","title":"SBCFormer: Lightweight Network Capable of Full-size ImageNet Classification at 1 FPS on Single Board Computers","date":"2023-11-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xyonglu/sbcformer","path":"models/mobilenetv2.py","file_url":"https://github.com/xyonglu/sbcformer/blob/HEAD/models/mobilenetv2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"408e747e0425a594","mcp_get_code":{"code_sha256":"408e747e0425a594"}},{"arxiv_id":"2307.00198","paper":"/paper/filter-pruning-for-efficient-cnns-via","title":"Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler","date":"2023-07-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"osilly/kdfs","path":"model/pruned_model/MobileNetV2_pruned.py","file_url":"https://github.com/osilly/kdfs/blob/HEAD/model/pruned_model/MobileNetV2_pruned.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"408e747e0425a594","mcp_get_code":{"code_sha256":"408e747e0425a594"}},{"arxiv_id":"2203.05180","paper":"/paper/knowledge-distillation-as-efficient-pre","title":"Knowledge Distillation as Efficient Pre-training: Faster Convergence, Higher Data-efficiency, and Better Transferability","date":"2022-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CVMI-Lab/KDEP","path":"src/mobilenet.py","file_url":"https://github.com/CVMI-Lab/KDEP/blob/HEAD/src/mobilenet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"408e747e0425a594","mcp_get_code":{"code_sha256":"408e747e0425a594"}},{"arxiv_id":"2106.04803","paper":"/paper/coatnet-marrying-convolution-and-attention","title":"CoAtNet: Marrying Convolution and Attention for All Data Sizes","date":"2021-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chinhsuanwu/coatnet-pytorch","path":"coatnet.py","file_url":"https://github.com/chinhsuanwu/coatnet-pytorch/blob/HEAD/coatnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c825b672e6f0b3bc","mcp_get_code":{"code_sha256":"c825b672e6f0b3bc"}},{"arxiv_id":"2104.00298","paper":"/paper/efficientnetv2-smaller-models-and-faster","title":"EfficientNetV2: Smaller Models and Faster Training","date":"2021-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"d-li14/efficientnetv2.pytorch","path":"effnetv2.py","file_url":"https://github.com/d-li14/efficientnetv2.pytorch/blob/HEAD/effnetv2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"70ced89b3b23f051","mcp_get_code":{"code_sha256":"70ced89b3b23f051"}},{"arxiv_id":"2007.02269","paper":"/paper/rethinking-bottleneck-structure-for-efficient","title":"Rethinking Bottleneck Structure for Efficient Mobile Network Design","date":"2020-07-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhoudaquan/rethinking_bottleneck_design","path":"mobile_deployment/pytorch/InvBlock/models/imagenet/i2rnet.py","file_url":"https://github.com/zhoudaquan/rethinking_bottleneck_design/blob/HEAD/mobile_deployment/pytorch/InvBlock/models/imagenet/i2rnet.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"408e747e0425a594","mcp_get_code":{"code_sha256":"408e747e0425a594"}},{"arxiv_id":"2007.02269","paper":"/paper/rethinking-bottleneck-structure-for-efficient","title":"Rethinking Bottleneck Structure for Efficient Mobile Network Design","date":"2020-07-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Andrew-Qibin/ssdlite-pytorch","path":"ssd/modeling/backbone/mobilenext.py","file_url":"https://github.com/Andrew-Qibin/ssdlite-pytorch/blob/HEAD/ssd/modeling/backbone/mobilenext.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"141234d4962cebd2","mcp_get_code":{"code_sha256":"141234d4962cebd2"}},{"arxiv_id":"2003.03771","paper":"/paper/pixel-in-pixel-net-towards-efficient-facial","title":"Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild","date":"2020-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jhb86253817/PIPNet","path":"lib/mobilenetv3.py","file_url":"https://github.com/jhb86253817/PIPNet/blob/HEAD/lib/mobilenetv3.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5221887077e3fb54","mcp_get_code":{"code_sha256":"5221887077e3fb54"}},{"arxiv_id":"1911.11907","paper":"/paper/ghostnet-more-features-from-cheap-operations","title":"GhostNet: More Features from Cheap Operations","date":"2019-11-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ozora-ogino/efficient_backbones","path":"efficient_backbones/efficientnet_v2.py","file_url":"https://github.com/ozora-ogino/efficient_backbones/blob/HEAD/efficient_backbones/efficientnet_v2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3d6f0eda22ace06f","mcp_get_code":{"code_sha256":"3d6f0eda22ace06f"}},{"arxiv_id":"1911.08114","paper":"/paper/neural-network-pruning-with-residual","title":"Neural Network Pruning with Residual-Connections and Limited-Data","date":"2019-11-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Roll920/CURL","path":"ImageNet/CURL/1_evaluate_filter_importance/mobilenetv2.py","file_url":"https://github.com/Roll920/CURL/blob/HEAD/ImageNet/CURL/1_evaluate_filter_importance/mobilenetv2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"408e747e0425a594","mcp_get_code":{"code_sha256":"408e747e0425a594"}},{"arxiv_id":"1905.02244","paper":"/paper/searching-for-mobilenetv3","title":"Searching for MobileNetV3","date":"2019-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wang-zidu/3ddfa-v3","path":"model/mb_v3_networks.py","file_url":"https://github.com/wang-zidu/3ddfa-v3/blob/HEAD/model/mb_v3_networks.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5221887077e3fb54","mcp_get_code":{"code_sha256":"5221887077e3fb54"}},{"arxiv_id":"1904.07399","paper":"/paper/adaptive-wing-loss-for-robust-face-alignment","title":"Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression","date":"2019-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"affromero/SMILE","path":"metrics/mobilenetv2.py","file_url":"https://github.com/affromero/SMILE/blob/HEAD/metrics/mobilenetv2.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"408e747e0425a594","mcp_get_code":{"code_sha256":"408e747e0425a594"}},{"arxiv_id":"1801.04381","paper":"/paper/mobilenetv2-inverted-residuals-and-linear","title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","date":"2018-01-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":null,"path":"","file_url":null,"status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":null,"inline_ok":false,"code_sha256_prefix":"408e747e0425a594","mcp_get_code":{"code_sha256":"408e747e0425a594"}},{"arxiv_id":"1512.02325","paper":"/paper/ssd-single-shot-multibox-detector","title":"SSD: Single Shot MultiBox Detector","date":"2015-12-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lufficc/SSD","path":"ssd/modeling/backbone/mobilenetv3.py","file_url":"https://github.com/lufficc/SSD/blob/HEAD/ssd/modeling/backbone/mobilenetv3.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5221887077e3fb54","mcp_get_code":{"code_sha256":"5221887077e3fb54"}},{"arxiv_id":"aaai_6622","paper":null,"title":"arXiv:aaai_6622","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"ouc-ocean-group/LDPS","path":"lib/backbone/mobile_net.py","file_url":"https://github.com/ouc-ocean-group/LDPS/blob/HEAD/lib/backbone/mobile_net.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"408e747e0425a594","mcp_get_code":{"code_sha256":"408e747e0425a594"}},{"arxiv_id":"Wang_3D_Face_Reconstruction_with_the_Geometric_Guidance_of_Facial_Part_CVPR_2024_paper","paper":null,"title":"arXiv:Wang_3D_Face_Reconstruction_with_the_Geometric_Guidance_of_Facial_Part_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"wang-zidu/3DDFA-V3","path":"model/mb_v3_networks.py","file_url":"https://github.com/wang-zidu/3DDFA-V3/blob/HEAD/model/mb_v3_networks.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5221887077e3fb54","mcp_get_code":{"code_sha256":"5221887077e3fb54"}},{"arxiv_id":"Cai_IIEU_Rethinking_Neural_Feature_Activation_from_Decision-Making_ICCV_2023_paper","paper":null,"title":"arXiv:Cai_IIEU_Rethinking_Neural_Feature_Activation_from_Decision-Making_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"SudongCAI/IIEU","path":"MODELS/raw_mobilenetv2_0dot17.py","file_url":"https://github.com/SudongCAI/IIEU/blob/HEAD/MODELS/raw_mobilenetv2_0dot17.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"408e747e0425a594","mcp_get_code":{"code_sha256":"408e747e0425a594"}},{"arxiv_id":"Cai_IIEU_Rethinking_Neural_Feature_Activation_from_Decision-Making_ICCV_2023_paper","paper":null,"title":"arXiv:Cai_IIEU_Rethinking_Neural_Feature_Activation_from_Decision-Making_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"SudongCAI/IIEU","path":"MODELS/gelu_mobilenetv2_0dot17.py","file_url":"https://github.com/SudongCAI/IIEU/blob/HEAD/MODELS/gelu_mobilenetv2_0dot17.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":"133a176d7f8c0c67","mcp_get_code":{"code_sha256":"133a176d7f8c0c67"}},{"arxiv_id":"Cai_IIEU_Rethinking_Neural_Feature_Activation_from_Decision-Making_ICCV_2023_paper","paper":null,"title":"arXiv:Cai_IIEU_Rethinking_Neural_Feature_Activation_from_Decision-Making_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"SudongCAI/IIEU","path":"MODELS/iieub_mobilenetv2_0dot17.py","file_url":"https://github.com/SudongCAI/IIEU/blob/HEAD/MODELS/iieub_mobilenetv2_0dot17.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":"734c2ccd66786225","mcp_get_code":{"code_sha256":"734c2ccd66786225"}},{"arxiv_id":"Cai_IIEU_Rethinking_Neural_Feature_Activation_from_Decision-Making_ICCV_2023_paper","paper":null,"title":"arXiv:Cai_IIEU_Rethinking_Neural_Feature_Activation_from_Decision-Making_ICCV_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"SudongCAI/IIEU","path":"MODELS/mish_mobilenetv2_0dot17.py","file_url":"https://github.com/SudongCAI/IIEU/blob/HEAD/MODELS/mish_mobilenetv2_0dot17.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":"f306d4a8b1a1b6af","mcp_get_code":{"code_sha256":"f306d4a8b1a1b6af"}},{"arxiv_id":"2023.acl-long.803","paper":null,"title":"arXiv:2023.acl-long.803","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Shwai-He/PAD-Net","path":"dyconv/model/mobilenetv2_dcd_pad.py","file_url":"https://github.com/Shwai-He/PAD-Net/blob/HEAD/dyconv/model/mobilenetv2_dcd_pad.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"408e747e0425a594","mcp_get_code":{"code_sha256":"408e747e0425a594"}},{"arxiv_id":"2023.acl-long.803","paper":null,"title":"arXiv:2023.acl-long.803","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Shwai-He/PAD-Net","path":"dyconv/model/mobilenetv2_dyconv_pad.py","file_url":"https://github.com/Shwai-He/PAD-Net/blob/HEAD/dyconv/model/mobilenetv2_dyconv_pad.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b743f1920f93d232","mcp_get_code":{"code_sha256":"b743f1920f93d232"}}]}