{"id":"W4386345866","doi":"10.21203/rs.3.rs-3299732/v1","title":"Real-time traffic sign detection network based on Swin Transformer","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Computer science; Transformer; Computation; Artificial intelligence; Traffic sign; Residual; Traffic sign recognition; Real-time computing; Sign (mathematics); Pattern recognition (psychology); Voltage; Engineering; Algorithm","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002429647,0.0006638457,0.0005740328,0.001189894,0.0002491693,0.0005272972,0.0008372519,0.0003349153,0.001702699],"category_scores_gemma":[0.0007263861,0.0001899107,0.000268013,0.0006586887,0.0002301181,0.0006699887,0.000663877,0.0004378686,0.0007776531],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006374017,"about_ca_system_score_gemma":0.0006578329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006892053,"about_ca_topic_score_gemma":0.009912181,"domain_scores_codex":[0.9998208,0.0000189035,0.000007339485,0.00005099765,0.00005685283,0.00004507765],"domain_scores_gemma":[0.9997383,0.00003927371,0.00002859491,0.00003348729,0.00012051,0.00003985271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001695083,0.0005136682,0.01722866,0.0001862975,0.0001446196,0.0005863531,0.000126402,0.1283023,0.1076675,0.002589553,0.01769461,0.7232649],"study_design_scores_gemma":[0.00001665395,0.00007502855,0.002653564,0.000004707345,0.00001781695,0.0001003443,0.00002899781,0.9712038,0.02399697,0.0005564809,0.001335994,0.000009602508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6728768,0.0003831891,0.3034365,0.0002919069,0.0002182548,0.0002338132,0.001320226,0.01318754,0.008051682],"genre_scores_gemma":[0.9359992,0.0001033845,0.05861171,0.00008229323,0.00002144918,0.00004829892,0.0021341,0.00009751105,0.002902063],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.006892053,"threshold_uncertainty_score":0.01370388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0705037209025731,"score_gpt":0.3716487848741072,"score_spread":0.3011450639715341,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}