{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005678396,0.001619609,0.001400616,0.00131793,0.0004580456,0.001311437,0.002519978,0.001306621,0.002809878],"category_scores_gemma":[0.0010644,0.0007157333,0.001758487,0.0006984823,0.0004499624,0.001207596,0.001579197,0.0009717756,0.002118328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001231542,"about_ca_system_score_gemma":0.001371672,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02231751,"about_ca_topic_score_gemma":0.0228427,"domain_scores_codex":[0.9995198,0.0000355112,0.00001646882,0.0001918085,0.0001380465,0.00009833668],"domain_scores_gemma":[0.9995862,0.00006308796,0.00005237074,0.00007240752,0.0001854605,0.00004063542],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001472828,0.0003449122,0.009366174,0.0003032954,0.0004759725,0.0004034633,0.0001394374,0.2739845,0.07714257,0.003395798,0.01799603,0.614975],"study_design_scores_gemma":[0.000009922915,0.00008021094,0.0008497915,0.00001262182,0.00003293489,0.00009987497,0.00001479573,0.9847955,0.0116988,0.0006782071,0.001712442,0.00001502335],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1319106,0.002529713,0.8345016,0.0005627924,0.0003671927,0.0002087421,0.002482687,0.02213061,0.005306061],"genre_scores_gemma":[0.6636133,0.001043257,0.310701,0.0008439969,0.0001192839,0.0002125438,0.00873424,0.0008825266,0.01384974],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02231751,"threshold_uncertainty_score":0.04437518,"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."}}