{"id":"W2501524975","doi":"10.1007/978-3-319-41501-7_41","title":"Object Detection and Localization Using Deep Convolutional Networks with Softmax Activation and Multi-class Log Loss","year":2016,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Softmax function; Computer science; Artificial intelligence; Minimum bounding box; Convolutional neural network; Pattern recognition (psychology); Region of interest; Computer vision; Deep learning; Object detection; Object (grammar); Contextual image classification; Image (mathematics)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002450679,0.0003765956,0.0002828581,0.0003550612,0.0004969513,0.000250782,0.0005446858,0.0002493588,0.000001804662],"category_scores_gemma":[0.00003318854,0.000305377,0.00002792832,0.0004968604,0.000934518,0.0008980234,0.000482233,0.0003896603,0.000001647445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002983716,"about_ca_system_score_gemma":0.0001452921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007524709,"about_ca_topic_score_gemma":0.0001020353,"domain_scores_codex":[0.9976128,0.00003238154,0.0003017097,0.001214113,0.0004294924,0.0004095381],"domain_scores_gemma":[0.9983982,0.0004033803,0.0003110263,0.0005129385,0.0002463129,0.0001281846],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001365695,0.00001150047,0.0002796145,0.00001497834,0.000008094227,0.000006515353,0.00009585296,0.5092049,0.0004993639,0.005687753,3.650289e-7,0.4841774],"study_design_scores_gemma":[0.0003581752,0.00008480479,0.0005626582,0.000222433,0.00000781857,0.0001524428,1.077387e-7,0.9742432,0.0007812052,0.02311122,0.0000879877,0.0003879291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003027365,0.0002560951,0.9983992,0.0001412412,0.0002609532,0.0004578658,0.000001657885,0.0001204724,0.00005975295],"genre_scores_gemma":[0.6805479,0.00008129921,0.3185211,0.0004838578,0.000284879,0.00001496377,0.000003364934,0.00003049752,0.00003217364],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6802452,"threshold_uncertainty_score":0.9999399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01630436585931179,"score_gpt":0.2411829195110352,"score_spread":0.2248785536517234,"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."}}