{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007118932,0.00150027,0.0009805416,0.002351306,0.0006797127,0.0006462507,0.002228687,0.001468146,0.01044322],"category_scores_gemma":[0.002167169,0.0004670473,0.001240396,0.001876967,0.0004871905,0.0008626505,0.001405337,0.001187161,0.01134754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007965462,"about_ca_system_score_gemma":0.0007946763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01364622,"about_ca_topic_score_gemma":0.02588858,"domain_scores_codex":[0.9989783,0.0001009217,0.00008375527,0.0003222736,0.0003760279,0.0001387459],"domain_scores_gemma":[0.9988571,0.0001484971,0.0000864104,0.0004708804,0.0003526366,0.00008435768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001145807,0.001220902,0.01208624,0.001830161,0.0003586296,0.0005834575,0.0002036675,0.01721327,0.04914029,0.003889207,0.6056261,0.3067023],"study_design_scores_gemma":[0.0005622945,0.001291526,0.1908927,0.0007110483,0.0003114646,0.007175846,0.0008285058,0.1869374,0.1223584,0.01185664,0.4765127,0.0005615443],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1163676,0.003406959,0.1360918,0.0008338862,0.0005640054,0.002109716,0.6906101,0.02711835,0.02289752],"genre_scores_gemma":[0.06625281,0.0007155509,0.08097624,0.0003183913,0.00007406594,0.001214201,0.8439018,0.0005507641,0.005996224],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01364622,"threshold_uncertainty_score":0.03493601,"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."}}