{"id":"W4385078307","doi":"10.18280/isi.280319","title":"Evaluation of Top Pretrained Models Using Transfer Learning on Banknote Dataset with Quality Parameter","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Currency Recognition and Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Banknote; Transfer of learning; Quality (philosophy); Computer science; Transfer (computing); Artificial intelligence; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.002905348,0.00271787,0.00136478,0.001463661,0.0005828153,0.001833852,0.001844376,0.001769268,0.003007913],"category_scores_gemma":[0.005258213,0.0004358192,0.001548692,0.00103779,0.0005370478,0.00233763,0.0009259559,0.002179505,0.002182165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00198344,"about_ca_system_score_gemma":0.001497224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02007136,"about_ca_topic_score_gemma":0.01628018,"domain_scores_codex":[0.9988116,0.0002498433,0.0001123463,0.0003531866,0.0002624204,0.00021058],"domain_scores_gemma":[0.998319,0.0005838153,0.0001175888,0.0003145389,0.0005367424,0.0001283579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.002201364,0.001289147,0.01591235,0.0009602281,0.0009997737,0.0005262198,0.000177725,0.5145821,0.01493803,0.001210191,0.03461054,0.4125924],"study_design_scores_gemma":[0.00005400462,0.0006132097,0.004715412,0.0001318207,0.0001543134,0.0001644641,0.000188366,0.9741965,0.0162455,0.0006952577,0.002787397,0.00005377797],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8510638,0.01500399,0.08843349,0.001783915,0.001961331,0.0004672065,0.006437551,0.02089314,0.01395557],"genre_scores_gemma":[0.9335173,0.002042768,0.04066925,0.0004639244,0.0001296095,0.0001757948,0.01595381,0.0005335861,0.006513897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02007136,"threshold_uncertainty_score":0.03990906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.119153561565441,"score_gpt":0.3276175598565155,"score_spread":0.2084639982910745,"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."}}