{"id":"W3216540427","doi":"10.36227/techrxiv.17004538.v1","title":"TFW: Annotated Thermal Faces in the Wild Dataset","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Face recognition and analysis","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Landmark; Computer science; Face (sociological concept); Minimum bounding box; Artificial intelligence; Benchmark (surveying); Bounding overwatch; Code (set theory); Face detection; Computer vision; Pattern recognition (psychology); Object detection; Facial recognition system; Set (abstract data type); Image (mathematics); Cartography; Geography","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":[0.0007596242,0.002540884,0.001224965,0.001806344,0.0008661188,0.0007840116,0.002336278,0.001671377,0.01551008],"category_scores_gemma":[0.00202616,0.0004557623,0.001322674,0.001327661,0.0005303596,0.0008820527,0.001353373,0.001374839,0.01568744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008589613,"about_ca_system_score_gemma":0.0006760905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01547877,"about_ca_topic_score_gemma":0.03437871,"domain_scores_codex":[0.9991252,0.0001094122,0.00005560162,0.0002667297,0.0003069473,0.0001361374],"domain_scores_gemma":[0.9994062,0.0000961124,0.0000459184,0.0002340897,0.0001676001,0.00005003947],"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":[0.001035132,0.0004936102,0.005250471,0.001224285,0.0002317786,0.0006764423,0.0001672977,0.003377076,0.01372185,0.001116783,0.8674948,0.1052104],"study_design_scores_gemma":[0.0008479162,0.001167042,0.1498879,0.001085487,0.0004150109,0.01138762,0.001291808,0.07868408,0.03937209,0.006835951,0.7085119,0.0005130661],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0712843,0.003760471,0.02663696,0.0005946208,0.001068064,0.001148729,0.8617933,0.01858054,0.01513301],"genre_scores_gemma":[0.04533712,0.0004582593,0.01876544,0.0002507445,0.00009140535,0.0008409694,0.9291655,0.0004765551,0.004614031],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01551008,"threshold_uncertainty_score":0.05188638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03258997642163258,"score_gpt":0.2780211141908055,"score_spread":0.2454311377691729,"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."}}