{"id":"W2742792558","doi":"10.1145/3063592","title":"Mobile Multi-Food Recognition Using Deep Learning","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Nutritional Studies and Diet","field":"Medicine","cited_by":85,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Minimum bounding box; Set (abstract data type); Deep learning; Cloud computing; Process (computing); Pattern recognition (psychology); Submodular set function; Machine learning; Computer vision; Image (mathematics); Mathematics","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":[0.000169436,0.001009807,0.0006992748,0.0007625264,0.0002097337,0.0004580788,0.001110193,0.0006854234,0.002007283],"category_scores_gemma":[0.0003994461,0.0003661483,0.0006310663,0.0007212245,0.0001849183,0.0008670957,0.0007306266,0.00053171,0.001303719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005449909,"about_ca_system_score_gemma":0.0003552785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007205789,"about_ca_topic_score_gemma":0.009832655,"domain_scores_codex":[0.9998073,0.0000136249,0.000008003863,0.00008368907,0.00004518572,0.00004224827],"domain_scores_gemma":[0.9998863,0.00002533321,0.0000183321,0.00001975266,0.00004132302,0.000008961668],"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":[0.0004676587,0.0003162227,0.005618124,0.0001761819,0.0001962929,0.0006104416,0.00005341724,0.1710135,0.03587013,0.001053074,0.008076143,0.7765489],"study_design_scores_gemma":[0.000009648663,0.00007258623,0.001483004,0.00001008536,0.00001930464,0.0001432628,0.0000201366,0.9880459,0.007316645,0.0009416097,0.001924983,0.00001284722],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1611333,0.003783208,0.8144575,0.0004916218,0.0003582202,0.000131453,0.001426159,0.01013676,0.008081755],"genre_scores_gemma":[0.7936268,0.0009670675,0.1917938,0.0005785616,0.00008242925,0.0001228923,0.003109397,0.0001322323,0.009586888],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007205789,"threshold_uncertainty_score":0.0143277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08922163145647409,"score_gpt":0.3552259678272213,"score_spread":0.2660043363707472,"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."}}