{"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/get-num-layer-for-convnext","entry":"get_num_layer_for_convnext","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":13,"n_papers_ran":0,"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":3,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":13,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":3},"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":"2412.01876","paper":"/paper/understanding-bias-in-large-scale-visual","title":"Understanding Bias in Large-Scale Visual Datasets","date":"2024-12-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"boyazeng/understand_bias","path":"optim_factory.py","file_url":"https://github.com/boyazeng/understand_bias/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"199223de2f9704cf","mcp_get_code":{"code_sha256":"199223de2f9704cf"}},{"arxiv_id":"2408.06741","paper":"/paper/improving-synthetic-image-detection-towards","title":"Improving Synthetic Image Detection Towards Generalization: An Image Transformation Perspective","date":"2024-08-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ouxiang-li/safe","path":"optim_factory.py","file_url":"https://github.com/ouxiang-li/safe/blob/HEAD/optim_factory.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":"199223de2f9704cf","mcp_get_code":{"code_sha256":"199223de2f9704cf"}},{"arxiv_id":"2408.03703","paper":"/paper/cas-vit-convolutional-additive-self-attention","title":"CAS-ViT: Convolutional Additive Self-attention Vision Transformers for Efficient Mobile Applications","date":"2024-08-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tianfang-zhang/cas-vit","path":"classification/optim_factory.py","file_url":"https://github.com/tianfang-zhang/cas-vit/blob/HEAD/classification/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"199223de2f9704cf","mcp_get_code":{"code_sha256":"199223de2f9704cf"}},{"arxiv_id":"2406.19435","paper":"/paper/a-sanity-check-for-ai-generated-image","title":"A Sanity Check for AI-generated Image Detection","date":"2024-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shilinyan99/aide","path":"optim_factory.py","file_url":"https://github.com/shilinyan99/aide/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"199223de2f9704cf","mcp_get_code":{"code_sha256":"199223de2f9704cf"}},{"arxiv_id":"2404.06773","paper":"/paper/adapting-llama-decoder-to-vision-transformer","title":"Adapting LLaMA Decoder to Vision Transformer","date":"2024-04-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"techmonsterwang/illama","path":"optim_factory.py","file_url":"https://github.com/techmonsterwang/illama/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"199223de2f9704cf","mcp_get_code":{"code_sha256":"199223de2f9704cf"}},{"arxiv_id":"2311.05589","paper":"/paper/a-coefficient-makes-svrg-effective","title":"A Coefficient Makes SVRG Effective","date":"2023-11-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"davidyyd/alpha-SVRG","path":"optim_factory.py","file_url":"https://github.com/davidyyd/alpha-SVRG/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"199223de2f9704cf","mcp_get_code":{"code_sha256":"199223de2f9704cf"}},{"arxiv_id":"2310.15171","paper":"/paper/robodepth-robust-out-of-distribution-depth-1","title":"RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions","date":"2023-10-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"noahzn/Lite-Mono","path":"lite-mono-pretrain-code/optim_factory.py","file_url":"https://github.com/noahzn/Lite-Mono/blob/HEAD/lite-mono-pretrain-code/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"199223de2f9704cf","mcp_get_code":{"code_sha256":"199223de2f9704cf"}},{"arxiv_id":"2309.15812","paper":"/paper/convolutional-networks-with-oriented-1d-1","title":"Convolutional Networks with Oriented 1D Kernels","date":"2023-09-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"princeton-vl/oriented1d","path":"optim_factory.py","file_url":"https://github.com/princeton-vl/oriented1d/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"199223de2f9704cf","mcp_get_code":{"code_sha256":"199223de2f9704cf"}},{"arxiv_id":"2304.02330","paper":"/paper/smpconv-self-moving-point-representations-for","title":"SMPConv: Self-moving Point Representations for Continuous Convolution","date":"2023-04-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sangnekim/SMPConv","path":"smp_imagenet/optim_factory.py","file_url":"https://github.com/sangnekim/SMPConv/blob/HEAD/smp_imagenet/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bf7fe9b696b6d730","mcp_get_code":{"code_sha256":"bf7fe9b696b6d730"}},{"arxiv_id":"2302.06052","paper":"/paper/cfnet-cascade-fusion-network-for-dense","title":"CEDNet: A Cascade Encoder-Decoder Network for Dense Prediction","date":"2023-02-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhanggang001/cfnet","path":"optim_factory.py","file_url":"https://github.com/zhanggang001/cfnet/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"9791d3412f2d6823","mcp_get_code":{"code_sha256":"9791d3412f2d6823"}},{"arxiv_id":"2211.05781","paper":"/paper/demystify-transformers-convolutions-in-modern","title":"Demystify Transformers & Convolutions in Modern Image Deep Networks","date":"2022-11-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opengvlab/stm-evaluation","path":"classification/optim_factory.py","file_url":"https://github.com/opengvlab/stm-evaluation/blob/HEAD/classification/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"199223de2f9704cf","mcp_get_code":{"code_sha256":"199223de2f9704cf"}},{"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":"xiaolai-sqlai/mobilenetv3","path":"optim_factory.py","file_url":"https://github.com/xiaolai-sqlai/mobilenetv3/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":false,"code_sha256_prefix":"199223de2f9704cf","mcp_get_code":{"code_sha256":"199223de2f9704cf"}},{"arxiv_id":"ijcai2023_0111","paper":null,"title":"arXiv:ijcai2023_0111","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"xiaolai-sqlai/RaMLP","path":"optim_factory.py","file_url":"https://github.com/xiaolai-sqlai/RaMLP/blob/HEAD/optim_factory.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"199223de2f9704cf","mcp_get_code":{"code_sha256":"199223de2f9704cf"}}]}