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accuracy.csv

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+tool,dataset,modality,metric,value,n,notes
+pyfeat_v2,disfaplus,au,f1_mean,0.5396,57150,"truth>=2, prob>=0.5; 12 AU"
+pyfeat_v2,disfaplus,au,f1_AU01,0.7837,57150,
+pyfeat_v2,disfaplus,au,f1_AU02,0.5674,57150,
+pyfeat_v2,disfaplus,au,f1_AU04,0.7079,57150,
+pyfeat_v2,disfaplus,au,f1_AU05,0.5788,57150,
+pyfeat_v2,disfaplus,au,f1_AU06,0.5858,57150,
+pyfeat_v2,disfaplus,au,f1_AU09,0.8252,57150,
+pyfeat_v2,disfaplus,au,f1_AU12,0.559,57150,
+pyfeat_v2,disfaplus,au,f1_AU15,0.649,57150,
+pyfeat_v2,disfaplus,au,f1_AU17,0.2816,57150,
+pyfeat_v2,disfaplus,au,f1_AU20,0.0119,57150,
+pyfeat_v2,disfaplus,au,f1_AU25,0.6682,57150,
+pyfeat_v2,disfaplus,au,f1_AU26,0.2563,57150,
+pyfeat_v1,disfaplus,au,f1_mean,0.2503,57150,"truth>=2, prob>=0.5; 12 AU"
+pyfeat_v1,disfaplus,au,f1_AU01,0.3393,57150,
+pyfeat_v1,disfaplus,au,f1_AU02,0.2361,57150,
+pyfeat_v1,disfaplus,au,f1_AU04,0.429,57150,
+pyfeat_v1,disfaplus,au,f1_AU05,0.0,57150,
+pyfeat_v1,disfaplus,au,f1_AU06,0.3213,57150,
+pyfeat_v1,disfaplus,au,f1_AU09,0.1334,57150,
+pyfeat_v1,disfaplus,au,f1_AU12,0.3927,57150,
+pyfeat_v1,disfaplus,au,f1_AU15,0.1179,57150,
+pyfeat_v1,disfaplus,au,f1_AU17,0.1368,57150,
+pyfeat_v1,disfaplus,au,f1_AU20,0.096,57150,
+pyfeat_v1,disfaplus,au,f1_AU25,0.6813,57150,
+pyfeat_v1,disfaplus,au,f1_AU26,0.1202,57150,
+openface3,disfaplus,au,f1_mean,0.4882,57668,8 AU predicted; missing AU treated as 0 over 12
+openface3,disfaplus,au,f1_AU01,0.8476,57668,
+openface3,disfaplus,au,f1_AU02,0.6899,57668,
+openface3,disfaplus,au,f1_AU04,0.8354,57668,
+openface3,disfaplus,au,f1_AU06,0.4202,57668,
+openface3,disfaplus,au,f1_AU09,0.8016,57668,
+openface3,disfaplus,au,f1_AU12,0.7375,57668,
+openface3,disfaplus,au,f1_AU25,0.9338,57668,
+openface3,disfaplus,au,f1_AU26,0.5919,57668,
+libreface,disfaplus,au,f1_mean,0.4614,57150,"research RepVGG; truth>=2, intensity>=2; 12 AU"
+libreface,disfaplus,au,f1_AU01,0.7031,57150,
+libreface,disfaplus,au,f1_AU02,0.7057,57150,
+libreface,disfaplus,au,f1_AU04,0.7462,57150,
+libreface,disfaplus,au,f1_AU05,0.6256,57150,
+libreface,disfaplus,au,f1_AU06,0.1116,57150,
+libreface,disfaplus,au,f1_AU09,0.7505,57150,
+libreface,disfaplus,au,f1_AU12,0.5632,57150,
+libreface,disfaplus,au,f1_AU15,0.0671,57150,
+libreface,disfaplus,au,f1_AU17,0.0648,57150,
+libreface,disfaplus,au,f1_AU20,0.0061,57150,
+libreface,disfaplus,au,f1_AU25,0.9065,57150,
+libreface,disfaplus,au,f1_AU26,0.2869,57150,
+libreface,disfaplus,au,pcc_AU01,0.7296,57150,
+libreface,disfaplus,au,pcc_AU02,0.7157,57150,
+libreface,disfaplus,au,pcc_AU04,0.8852,57150,
+libreface,disfaplus,au,pcc_AU05,0.8449,57150,
+libreface,disfaplus,au,pcc_AU06,0.7509,57150,
+libreface,disfaplus,au,pcc_AU09,0.8643,57150,
+libreface,disfaplus,au,pcc_AU12,0.8451,57150,
+libreface,disfaplus,au,pcc_AU15,0.5913,57150,
+libreface,disfaplus,au,pcc_AU17,0.6065,57150,
+libreface,disfaplus,au,pcc_AU20,0.2439,57150,
+libreface,disfaplus,au,pcc_AU25,0.9421,57150,
+libreface,disfaplus,au,pcc_AU26,0.7767,57150,
+pyfeat_v2,affectnet_val,emotion,accuracy,0.4919,992,
+pyfeat_v2,affectnet_val,emotion,f1_macro,0.4787,992,
+pyfeat_v2,affectnet_val,valence_arousal,valence_ccc,0.5349,992,
+pyfeat_v2,affectnet_val,valence_arousal,arousal_ccc,0.4822,992,
+pyfeat_v2,raf_test,emotion,accuracy,0.6555,3019,
+pyfeat_v2,raf_test,emotion,f1_macro,0.5275,3019,
+pyfeat_v2,columbia_gaze,gaze,angular_mae_deg,2.721,1176,"a=pitch,b=yaw;sign_pitch=1;sign_yaw=-1;unit=rad"
+openface3,affectnet_val,emotion,accuracy,0.4929,980,
+openface3,affectnet_val,emotion,f1_macro,0.5198,980,
+openface3,raf_test,emotion,accuracy,0.5132,2233,
+openface3,raf_test,emotion,f1_macro,0.469,2233,
+openface3,columbia_gaze,gaze,angular_mae_deg,12.049,1176,"a=pitch,b=yaw;sign_pitch=1;sign_yaw=1;unit=deg"
+libreface,affectnet_val,emotion,accuracy,0.4553,984,
+libreface,affectnet_val,emotion,f1_macro,0.4026,984,
+libreface,raf_test,emotion,accuracy,0.6458,2705,
+libreface,raf_test,emotion,f1_macro,0.3855,2705,
+libreface,columbia_gaze,gaze,angular_mae_deg,15.401,1176,"a=yaw,b=pitch;sign_pitch=1;sign_yaw=1;unit=deg"