{"id":"W3109039129","doi":"10.18280/isi.250517","title":"Object Detection Using Stacked YOLOv3","year":2020,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bounding overwatch; Computer science; Object detection; Artificial intelligence; Minimum bounding box; Convolutional neural network; Intersection (aeronautics); Hyperparameter; Task (project management); Pattern recognition (psychology); Object (grammar); Deep learning; Margin (machine learning); Computer vision; Convolution (computer science); Artificial neural network; Machine learning; Image (mathematics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001080579,0.0001368763,0.000132255,0.00009649016,0.0003086809,0.000243928,0.0004089119,0.00006347044,0.000006631158],"category_scores_gemma":[0.0001207464,0.000141392,0.00004998565,0.0009602426,0.0000542073,0.005055506,0.0001494869,0.0001315963,0.0001594656],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001764059,"about_ca_system_score_gemma":0.00004963989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001451575,"about_ca_topic_score_gemma":0.000002696465,"domain_scores_codex":[0.998942,0.00004011301,0.0003947325,0.0001675944,0.0002107478,0.000244877],"domain_scores_gemma":[0.9991553,0.00004395503,0.0002487713,0.0002846958,0.0001521574,0.0001151031],"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":[0.00006251137,0.00003146972,0.000605952,0.0002864177,0.00005198994,0.000005581172,0.01892899,0.08392985,0.02731175,0.03111382,0.0003729833,0.8372987],"study_design_scores_gemma":[0.0002859,0.00009906721,0.001182106,0.00002568603,0.000008011982,0.00003958691,0.0002148111,0.9664479,0.01884943,0.006419354,0.006146232,0.0002819452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1004344,0.00002820094,0.8972843,0.0001689126,0.0001460807,0.0002774486,0.000003349262,0.0004850756,0.001172219],"genre_scores_gemma":[0.9519596,0.00000856931,0.04709366,0.0007958515,0.00008831455,0.00003172515,0.00001057662,0.000007508208,0.000004162585],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.882518,"threshold_uncertainty_score":0.57658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02941936145044345,"score_gpt":0.2517203979680687,"score_spread":0.2223010365176253,"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."}}