{"id":"W2955582135","doi":"10.48550/arxiv.1906.11942","title":"Datasets for Face and Object Detection in Fisheye Images","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Face (sociological concept); Computer science; Computer vision; Object-class detection; MATLAB; Face detection; Object detection; Object (grammar); Segmentation; Viola–Jones object detection framework; Pattern recognition (psychology); Facial recognition system","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":[],"consensus_categories":[],"category_scores_codex":[0.000176708,0.0001616808,0.0002228785,0.0003128239,0.00006283259,0.0001205515,0.000518523,0.0001454898,0.000007257471],"category_scores_gemma":[0.00003364801,0.0001879592,0.0001041518,0.0003340589,0.00003366105,0.0003611264,0.0006991418,0.0002270624,0.00003177515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007517163,"about_ca_system_score_gemma":0.00005079046,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001530926,"about_ca_topic_score_gemma":0.000280036,"domain_scores_codex":[0.9988391,0.00006736789,0.0001112741,0.0007551724,0.00004187931,0.000185217],"domain_scores_gemma":[0.9991606,0.00009854278,0.0001074055,0.0005193477,0.00004932168,0.0000647433],"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":[0.0003982795,0.00102203,0.03645995,0.002919879,0.001225369,0.0007719644,0.001733191,0.7248619,0.004320845,0.02030219,0.00855417,0.1974302],"study_design_scores_gemma":[0.0007136939,0.00004323015,0.003034132,0.00007492613,0.00006313623,0.000002798231,0.0001115066,0.9859799,0.001878617,0.006845008,0.0008668306,0.0003862295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1251331,0.00004733666,0.8736027,0.0001002667,0.0001977168,0.0003036947,0.0002054881,0.00007547028,0.0003341461],"genre_scores_gemma":[0.9981769,0.0002345835,0.0009210531,0.00006210985,0.00001471595,0.000001374125,0.0000972908,0.000006600117,0.0004853856],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8730438,"threshold_uncertainty_score":0.7664753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04946523705369637,"score_gpt":0.1946979973908465,"score_spread":0.1452327603371501,"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."}}