{"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-1x1-bn","entry":"conv_1x1_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":59,"n_papers_ran":54,"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":23,"n_samples_ran":11,"n_samples_fingerprinted":0,"n_places":72,"n_places_pointer_only":13,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":8,"ran_fixture":0,"ran":3,"unverified":12},"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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"arxiv_id":"2601.22537","paper":"/paper/arxiv-2601-22537","title":"EndoCaver: Handling Fog, Blur and Glare in Endoscopic Images via Joint Deblurring-Segmentation","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"ReaganWu/EndoCaver","path":"endocaver/model.py","file_url":"https://github.com/ReaganWu/EndoCaver/blob/HEAD/endocaver/model.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":"905aac0dbd683010","mcp_get_code":{"code_sha256":"905aac0dbd683010"}},{"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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"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":"4e90491a30c2dc28","mcp_get_code":{"code_sha256":"4e90491a30c2dc28"}},{"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":"e59ffdb9614460c4","mcp_get_code":{"code_sha256":"e59ffdb9614460c4"}},{"arxiv_id":"2407.06504","paper":"/paper/reprogramming-distillation-for-medical","title":"Reprogramming Distillation for Medical Foundation Models","date":"2024-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MediaBrain-SJTU/RD","path":"network/mobilenet_v2.py","file_url":"https://github.com/MediaBrain-SJTU/RD/blob/HEAD/network/mobilenet_v2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ea8083eb90046a26","mcp_get_code":{"code_sha256":"ea8083eb90046a26"}},{"arxiv_id":"2405.16749","paper":"/paper/dmplug-a-plug-in-method-for-solving-inverse","title":"DMPlug: A Plug-in Method for Solving Inverse Problems with Diffusion Models","date":"2024-05-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VITA-Group/DeblurGANv2","path":"models/mobilenet_v2.py","file_url":"https://github.com/VITA-Group/DeblurGANv2/blob/HEAD/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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"arxiv_id":"2402.11148","paper":"/paper/knowledge-distillation-based-on-transformed","title":"Knowledge Distillation Based on Transformed Teacher Matching","date":"2024-02-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zkxufo/TTM","path":"models/mobilenetv2.py","file_url":"https://github.com/zkxufo/TTM/blob/HEAD/models/mobilenetv2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7765583b9e540679","mcp_get_code":{"code_sha256":"7765583b9e540679"}},{"arxiv_id":"2401.10541","paper":"/paper/i-splitee-image-classification-in-split","title":"I-SplitEE: Image classification in Split Computing DNNs with Early Exits","date":"2024-01-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Div290/I-SplitEE","path":"b_mobilenet.py","file_url":"https://github.com/Div290/I-SplitEE/blob/HEAD/b_mobilenet.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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"arxiv_id":"2303.11906","paper":"/paper/solving-oscillation-problem-in-post-training","title":"Solving Oscillation Problem in Post-Training Quantization Through a Theoretical Perspective","date":"2023-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bytedance/mrecg","path":"models/mobilenet_v2.py","file_url":"https://github.com/bytedance/mrecg/blob/HEAD/models/mobilenet_v2.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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"arxiv_id":"2303.06870","paper":"/paper/three-guidelines-you-should-know-for","title":"Three Guidelines You Should Know for Universally Slimmable Self-Supervised Learning","date":"2023-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"megvii-research/us3l-cvpr2023","path":"models/mobilenetv2.py","file_url":"https://github.com/megvii-research/us3l-cvpr2023/blob/HEAD/models/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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"arxiv_id":"2211.16231","paper":"/paper/curriculum-temperature-for-knowledge","title":"Curriculum Temperature for Knowledge Distillation","date":"2022-11-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhengli97/ctkd","path":"models/mobilenetv2.py","file_url":"https://github.com/zhengli97/ctkd/blob/HEAD/models/mobilenetv2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7765583b9e540679","mcp_get_code":{"code_sha256":"7765583b9e540679"}},{"arxiv_id":"2209.04996","paper":"/paper/switchable-online-knowledge-distillation","title":"Switchable Online Knowledge Distillation","date":"2022-09-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hfutqian/SwitOKD","path":"SwitOKD_code/CIFAR-100/WRN16-2_0.5mobilenetv2/models/mobilenetv2.py","file_url":"https://github.com/hfutqian/SwitOKD/blob/HEAD/SwitOKD_code/CIFAR-100/WRN16-2_0.5mobilenetv2/models/mobilenetv2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7765583b9e540679","mcp_get_code":{"code_sha256":"7765583b9e540679"}},{"arxiv_id":"2205.06701","paper":"/paper/knowledge-distillation-meets-open-set-semi","title":"Knowledge Distillation Meets Open-Set Semi-Supervised Learning","date":"2022-05-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jingyang2017/srd_ossl","path":"models/mobilenetv2.py","file_url":"https://github.com/jingyang2017/srd_ossl/blob/HEAD/models/mobilenetv2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7765583b9e540679","mcp_get_code":{"code_sha256":"7765583b9e540679"}},{"arxiv_id":"2203.13611","paper":"/paper/class-incremental-learning-for-action-1","title":"Class-Incremental Learning for Action Recognition in Videos","date":"2022-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mit-han-lab/temporal-shift-module","path":"archs/mobilenet_v2.py","file_url":"https://github.com/mit-han-lab/temporal-shift-module/blob/HEAD/archs/mobilenet_v2.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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"arxiv_id":"2112.07133","paper":"/paper/clip-lite-information-efficient-visual","title":"CLIP-Lite: Information Efficient Visual Representation Learning with Language Supervision","date":"2021-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"4m4n5/CLIP-Lite","path":"model_zoo/mobilenetv2.py","file_url":"https://github.com/4m4n5/CLIP-Lite/blob/HEAD/model_zoo/mobilenetv2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7765583b9e540679","mcp_get_code":{"code_sha256":"7765583b9e540679"}},{"arxiv_id":"2110.09057","paper":"/paper/training-deep-neural-networks-with-adaptive","title":"Training Deep Neural Networks with Adaptive Momentum Inspired by the Quadratic Optimization","date":"2021-10-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kentaroy47/vision-transformers-cifar10","path":"models/mobilevit.py","file_url":"https://github.com/kentaroy47/vision-transformers-cifar10/blob/HEAD/models/mobilevit.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":"3fb34464dacbb0d3","mcp_get_code":{"code_sha256":"3fb34464dacbb0d3"}},{"arxiv_id":"2110.02178","paper":"/paper/mobilevit-light-weight-general-purpose-and","title":"MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer","date":"2021-10-05","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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"arxiv_id":"2110.02178","paper":"/paper/mobilevit-light-weight-general-purpose-and","title":"MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer","date":"2021-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chinhsuanwu/mobilevit-pytorch","path":"mobilevit.py","file_url":"https://github.com/chinhsuanwu/mobilevit-pytorch/blob/HEAD/mobilevit.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":"3fb34464dacbb0d3","mcp_get_code":{"code_sha256":"3fb34464dacbb0d3"}},{"arxiv_id":"2110.02178","paper":"/paper/mobilevit-light-weight-general-purpose-and","title":"MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer","date":"2021-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kornia/kornia","path":"kornia/models/vit_mobile.py","file_url":"https://github.com/kornia/kornia/blob/HEAD/kornia/models/vit_mobile.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"564f22f3b28db627","mcp_get_code":{"code_sha256":"564f22f3b28db627"}},{"arxiv_id":"2110.02178","paper":"/paper/mobilevit-light-weight-general-purpose-and","title":"MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer","date":"2021-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chinhsuanwu/mobilevit-pytorch","path":"mobilevit.py","file_url":"https://github.com/chinhsuanwu/mobilevit-pytorch/blob/HEAD/mobilevit.py","status":"unverified","verification_level":0,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"248f0c82db73b84d","mcp_get_code":{"code_sha256":"248f0c82db73b84d"}},{"arxiv_id":"2106.07849","paper":"/paper/simon-says-evaluating-and-mitigating-bias-in","title":"Simon Says: Evaluating and Mitigating Bias in Pruned Neural Networks with Knowledge Distillation","date":"2021-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"codestar12/pruning-distilation-bias","path":"models/mobilenetv2.py","file_url":"https://github.com/codestar12/pruning-distilation-bias/blob/HEAD/models/mobilenetv2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"7765583b9e540679","mcp_get_code":{"code_sha256":"7765583b9e540679"}},{"arxiv_id":"2106.06799","paper":"/paper/zero-cost-proxies-meet-differentiable","title":"Zero-Cost Operation Scoring in Differentiable Architecture Search","date":"2021-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zerocostptnas/zerocost_operation_score","path":"mobilenet_search_space/retrain_architecture/model.py","file_url":"https://github.com/zerocostptnas/zerocost_operation_score/blob/HEAD/mobilenet_search_space/retrain_architecture/model.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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"arxiv_id":"2106.04570","paper":"/paper/meta-learning-for-knowledge-distillation","title":"BERT Learns to Teach: Knowledge Distillation with Meta Learning","date":"2021-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JetRunner/MetaDistil","path":"cv/models/mobilenetv2.py","file_url":"https://github.com/JetRunner/MetaDistil/blob/HEAD/cv/models/mobilenetv2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7765583b9e540679","mcp_get_code":{"code_sha256":"7765583b9e540679"}},{"arxiv_id":"2106.01603","paper":"/paper/ct-net-channel-tensorization-network-for-1","title":"CT-Net: Channel Tensorization Network for Video Classification","date":"2021-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Andy1621/CT-Net","path":"ops/reshape_block.py","file_url":"https://github.com/Andy1621/CT-Net/blob/HEAD/ops/reshape_block.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":"c455a6669e2554a5","mcp_get_code":{"code_sha256":"c455a6669e2554a5"}},{"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":"15be99ad0bcca709","mcp_get_code":{"code_sha256":"15be99ad0bcca709"}},{"arxiv_id":"2009.11072","paper":"/paper/differential-viewpoints-for-ground-terrain","title":"Differential Viewpoints for Ground Terrain Material Recognition","date":"2020-09-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiaxue1993/pytorch-material-classification","path":"model/mobilenet_v2.py","file_url":"https://github.com/jiaxue1993/pytorch-material-classification/blob/HEAD/model/mobilenet_v2.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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"arxiv_id":"2009.09960","paper":"/paper/towards-fast-accurate-and-stable-3d-dense-1","title":"Towards Fast, Accurate and Stable 3D Dense Face Alignment","date":"2020-09-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cleardusk/3DDFA_V2","path":"models/mobilenet_v3.py","file_url":"https://github.com/cleardusk/3DDFA_V2/blob/HEAD/models/mobilenet_v3.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":"db82a4013a44fb42","mcp_get_code":{"code_sha256":"db82a4013a44fb42"}},{"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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"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":"778b1206838f15e8","mcp_get_code":{"code_sha256":"778b1206838f15e8"}},{"arxiv_id":"2004.13431","paper":"/paper/angle-based-search-space-shrinking-for-neural","title":"Angle-based Search Space Shrinking for Neural Architecture Search","date":"2020-04-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"megvii-model/AngleNAS","path":"FairNAS/searching/super_model.py","file_url":"https://github.com/megvii-model/AngleNAS/blob/HEAD/FairNAS/searching/super_model.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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"arxiv_id":"2003.05477","paper":"/paper/unified-image-and-video-saliency-modeling","title":"Unified Image and Video Saliency Modeling","date":"2020-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rdroste/unisal","path":"unisal/models/MobileNetV2.py","file_url":"https://github.com/rdroste/unisal/blob/HEAD/unisal/models/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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"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":"4e90491a30c2dc28","mcp_get_code":{"code_sha256":"4e90491a30c2dc28"}},{"arxiv_id":"2001.00705","paper":"/paper/fractional-skipping-towards-finer-grained","title":"Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference","date":"2020-01-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mit-han-lab/haq-release","path":"models/mobilenetv3.py","file_url":"https://github.com/mit-han-lab/haq-release/blob/HEAD/models/mobilenetv3.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":"db82a4013a44fb42","mcp_get_code":{"code_sha256":"db82a4013a44fb42"}},{"arxiv_id":"2001.00705","paper":"/paper/fractional-skipping-towards-finer-grained","title":"Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference","date":"2020-01-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mit-han-lab/haq-release","path":"models/mobilenetv2.py","file_url":"https://github.com/mit-han-lab/haq-release/blob/HEAD/models/mobilenetv2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bc660f7845db5634","mcp_get_code":{"code_sha256":"bc660f7845db5634"}},{"arxiv_id":"2001.00281","paper":"/paper/zeroq-a-novel-zero-shot-quantization","title":"ZeroQ: A Novel Zero Shot Quantization Framework","date":"2020-01-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jakc4103/DFQ","path":"modeling/classification/MobileNetV2.py","file_url":"https://github.com/jakc4103/DFQ/blob/HEAD/modeling/classification/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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"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":"f76badd0c6766f8a","mcp_get_code":{"code_sha256":"f76badd0c6766f8a"}},{"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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"arxiv_id":"1908.09124","paper":"/paper/seesawfacenets-sparse-and-robust-face","title":"SeesawFaceNets: sparse and robust face verification model for mobile platform","date":"2019-08-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cvtower/SeesawNet_pytorch","path":"code/models/imagenet/seesaw.py","file_url":"https://github.com/cvtower/SeesawNet_pytorch/blob/HEAD/code/models/imagenet/seesaw.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":"0db9f814cdec6488","mcp_get_code":{"code_sha256":"0db9f814cdec6488"}},{"arxiv_id":"1908.01748","paper":"/paper/squeezenas-fast-neural-architecture-search","title":"SqueezeNAS: Fast neural architecture search for faster semantic segmentation","date":"2019-08-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ashaw596/squeezenas","path":"arch/utils.py","file_url":"https://github.com/ashaw596/squeezenas/blob/HEAD/arch/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2e73e8e10fb7ce2e","mcp_get_code":{"code_sha256":"2e73e8e10fb7ce2e"}},{"arxiv_id":"1906.06579","paper":"/paper/extd-extremely-tiny-face-detector-via","title":"EXTD: Extremely Tiny Face Detector via Iterative Filter Reuse","date":"2019-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"clovaai/EXTD_Pytorch","path":"mobileFacenet_32_PReLU.py","file_url":"https://github.com/clovaai/EXTD_Pytorch/blob/HEAD/mobileFacenet_32_PReLU.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"20c32d5668430dcd","mcp_get_code":{"code_sha256":"20c32d5668430dcd"}},{"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":"4e90491a30c2dc28","mcp_get_code":{"code_sha256":"4e90491a30c2dc28"}},{"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":"chris-boson/fashion_mnist","path":"trainer/models/mobilenetv3.py","file_url":"https://github.com/chris-boson/fashion_mnist/blob/HEAD/trainer/models/mobilenetv3.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"db82a4013a44fb42","mcp_get_code":{"code_sha256":"db82a4013a44fb42"}},{"arxiv_id":"1904.12368","paper":"/paper/legr-filter-pruning-via-learned-global","title":"Towards Efficient Model Compression via Learned Global Ranking","date":"2019-04-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cmu-enyac/LeGR","path":"model/MobileNetV2.py","file_url":"https://github.com/cmu-enyac/LeGR/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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"arxiv_id":"1812.11703","paper":"/paper/siamrpn-evolution-of-siamese-visual-tracking","title":"SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks","date":"2018-12-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"STVIR/pysot","path":"pysot/models/backbone/mobile_v2.py","file_url":"https://github.com/STVIR/pysot/blob/HEAD/pysot/models/backbone/mobile_v2.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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"arxiv_id":"1812.08008","paper":"/paper/openpose-realtime-multi-person-2d-pose","title":"OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields","date":"2018-12-18","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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"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":"ZTao-z/ProxylessNAS","path":"training/net224x224/mobilenetv2.py","file_url":"https://github.com/ZTao-z/ProxylessNAS/blob/HEAD/training/net224x224/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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"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/haq","path":"models/mobilenetv3.py","file_url":"https://github.com/mit-han-lab/haq/blob/HEAD/models/mobilenetv3.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":"db82a4013a44fb42","mcp_get_code":{"code_sha256":"db82a4013a44fb42"}},{"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/haq","path":"models/mobilenetv2.py","file_url":"https://github.com/mit-han-lab/haq/blob/HEAD/models/mobilenetv2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bc660f7845db5634","mcp_get_code":{"code_sha256":"bc660f7845db5634"}},{"arxiv_id":"1811.08383","paper":"/paper/temporal-shift-module-for-efficient-video","title":"TSM: Temporal Shift Module for Efficient Video Understanding","date":"2018-11-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunutf/TSM","path":"online_demo/mobilenet_v2_tsm.py","file_url":"https://github.com/sunutf/TSM/blob/HEAD/online_demo/mobilenet_v2_tsm.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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"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/shufflenetv2.py","file_url":"https://github.com/SonpKing/semantic-segmentation-pytorch/blob/HEAD/models/shufflenetv2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7765583b9e540679","mcp_get_code":{"code_sha256":"7765583b9e540679"}},{"arxiv_id":"1806.00178","paper":"/paper/igcv3-interleaved-low-rank-group-convolutions","title":"IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks","date":"2018-06-01","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":"0db9f814cdec6488","mcp_get_code":{"code_sha256":"0db9f814cdec6488"}},{"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":"0db9f814cdec6488","mcp_get_code":{"code_sha256":"0db9f814cdec6488"}},{"arxiv_id":"1703.07402","paper":"/paper/simple-online-and-realtime-tracking-with-a","title":"Simple Online and Realtime Tracking with a Deep Association Metric","date":"2017-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"levan92/deep_sort_realtime","path":"deep_sort_realtime/embedder/mobilenetv2_bottle.py","file_url":"https://github.com/levan92/deep_sort_realtime/blob/HEAD/deep_sort_realtime/embedder/mobilenetv2_bottle.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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"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":"4e90491a30c2dc28","mcp_get_code":{"code_sha256":"4e90491a30c2dc28"}},{"arxiv_id":"1503.03832","paper":"/paper/facenet-a-unified-embedding-for-face","title":"FaceNet: A Unified Embedding for Face Recognition and Clustering","date":"2015-03-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"altndrr/persona","path":"src/models/mobilenet.py","file_url":"https://github.com/altndrr/persona/blob/HEAD/src/models/mobilenet.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":"db82a4013a44fb42","mcp_get_code":{"code_sha256":"db82a4013a44fb42"}},{"arxiv_id":"ijcai2022_0190","paper":null,"title":"arXiv:ijcai2022_0190","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"OverEuro/deep-head-pose-lite","path":"hopenetlite_v2.py","file_url":"https://github.com/OverEuro/deep-head-pose-lite/blob/HEAD/hopenetlite_v2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"7765583b9e540679","mcp_get_code":{"code_sha256":"7765583b9e540679"}},{"arxiv_id":"ijcai2022_0132","paper":null,"title":"arXiv:ijcai2022_0132","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"woshidandan/TANet","path":"code/AVA/train_nni.py","file_url":"https://github.com/woshidandan/TANet/blob/HEAD/code/AVA/train_nni.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":"c9d11a5f9d03ed48","mcp_get_code":{"code_sha256":"c9d11a5f9d03ed48"}},{"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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"arxiv_id":"aaai_20232","paper":null,"title":"arXiv:aaai_20232","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"zhanxinrui/HDN","path":"hdn/models/backbone/mobile_v2.py","file_url":"https://github.com/zhanxinrui/HDN/blob/HEAD/hdn/models/backbone/mobile_v2.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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"arxiv_id":"aaai_16351","paper":null,"title":"arXiv:aaai_16351","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"taoxvzi/DPFPS","path":"models/mobilenetv2.py","file_url":"https://github.com/taoxvzi/DPFPS/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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"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":"4e90491a30c2dc28","mcp_get_code":{"code_sha256":"4e90491a30c2dc28"}},{"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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"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":"cbdb98dab99b0dd4","mcp_get_code":{"code_sha256":"cbdb98dab99b0dd4"}},{"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":"d78333f34779d332","mcp_get_code":{"code_sha256":"d78333f34779d332"}},{"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":"93e2c1d1c0920215","mcp_get_code":{"code_sha256":"93e2c1d1c0920215"}},{"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":"a0131fb70c267a9e","mcp_get_code":{"code_sha256":"a0131fb70c267a9e"}},{"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":"25a027c38cff5603","mcp_get_code":{"code_sha256":"25a027c38cff5603"}}]}