{"id":"W2962845546","doi":"10.1109/crv.2018.00023","title":"Tiny SSD: A Tiny Single-Shot Detection Deep Convolutional Neural Network for Real-Time Embedded Object Detection","year":2018,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":174,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Object detection; Computer science; Convolutional neural network; Subnetwork; Artificial intelligence; Deep learning; Object (grammar); Artificial neural network; Computer vision; Stack (abstract data type); Embedded system; Pattern recognition (psychology); Computer network","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":[0.000267148,0.0002711327,0.0002317261,0.0001070304,0.0007930425,0.0001378342,0.000562642,0.000140533,0.00005838839],"category_scores_gemma":[0.00007655183,0.0002693998,0.0001406006,0.0009976616,0.0001532491,0.0006384923,0.0001807371,0.0001620087,0.0002117106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001747326,"about_ca_system_score_gemma":0.00003955289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002459428,"about_ca_topic_score_gemma":0.0003616214,"domain_scores_codex":[0.9977267,0.00009305478,0.0004244141,0.0007893143,0.0002828877,0.0006835623],"domain_scores_gemma":[0.9981698,0.0004045284,0.0002369963,0.000670491,0.0003549821,0.0001632458],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003063256,0.0002190665,0.00008148143,0.00002008026,0.00006757703,0.000003132306,0.0003478298,0.02015172,0.6998016,0.01034019,0.003757238,0.2649038],"study_design_scores_gemma":[0.0004364813,0.0006383946,0.0007066887,0.00000835877,0.00001800301,0.00007347213,0.00001285318,0.9243246,0.06097157,0.008715915,0.003729679,0.0003639981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03503742,0.00002595384,0.9589982,0.0002485437,0.0007924272,0.0008449191,0.000003802105,0.0009905293,0.00305818],"genre_scores_gemma":[0.8713526,0.000005304883,0.1253888,0.0004085602,0.001767572,0.0002868296,0.00001532072,0.00003431815,0.000740752],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9041728,"threshold_uncertainty_score":0.9999758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02606197856165662,"score_gpt":0.2702069869112208,"score_spread":0.2441450083495642,"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."}}