{"id":"W2920647791","doi":"10.17577/ijertv7is060085","title":"Object Recognition Using Deep Learning","year":2018,"lang":"en","type":"article","venue":"International Journal of Engineering Research and","topic":"Industrial Vision Systems and Defect Detection","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Cognitive neuroscience of visual object recognition; Object (grammar); Pattern recognition (psychology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005119728,0.0007809097,0.0007445563,0.0009987174,0.0002319495,0.001339419,0.001165105,0.0009700716,0.003856492],"category_scores_gemma":[0.001326433,0.0003373477,0.0007562509,0.001141447,0.0004311325,0.00172369,0.001252763,0.001028052,0.003351279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006337913,"about_ca_system_score_gemma":0.0006272628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0041445,"about_ca_topic_score_gemma":0.005165545,"domain_scores_codex":[0.9995527,0.00004768823,0.00002671019,0.0001394998,0.0001823391,0.00005108361],"domain_scores_gemma":[0.9996333,0.00008945472,0.00003967716,0.0001301953,0.0000892852,0.00001808589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008226098,0.0001057785,0.001440435,0.0003006847,0.0001522143,0.0001061448,0.00004428511,0.1067796,0.03119193,0.01297089,0.01115167,0.8356741],"study_design_scores_gemma":[0.000009321462,0.00007640476,0.001379873,0.00006078965,0.00003051441,0.0001447432,0.00002820422,0.9330888,0.02094047,0.02768541,0.01652918,0.00002629847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01841111,0.00306546,0.9649429,0.0004555285,0.0002021998,0.00008978983,0.0009749536,0.005001171,0.006856742],"genre_scores_gemma":[0.4373559,0.004458548,0.5378872,0.0006408801,0.0002204968,0.0002004756,0.005807957,0.0002798974,0.01314862],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0041445,"threshold_uncertainty_score":0.01290119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07558359506352623,"score_gpt":0.3443859601039807,"score_spread":0.2688023650404545,"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."}}