Create metrics.log

metrics.log

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+Subset ('m0',) accuracies 
+{'m1': 0.6014, 'm2': 0.5859, 'm3': 0.5755, 'm4': 0.5534}
+Mean subset ('m0',) accuracies : 0.57905
+Subset ('m1',) accuracies 
+{'m0': 0.4711, 'm2': 0.7107, 'm3': 0.6988, 'm4': 0.6677}
+Mean subset ('m1',) accuracies : 0.637075
+Subset ('m2',) accuracies 
+{'m0': 0.4738, 'm1': 0.7181, 'm3': 0.6828, 'm4': 0.6576}
+Mean subset ('m2',) accuracies : 0.6330749999999999
+Subset ('m3',) accuracies 
+{'m0': 0.4751, 'm1': 0.7236, 'm2': 0.7194, 'm4': 0.6513}
+Mean subset ('m3',) accuracies : 0.64235
+Subset ('m4',) accuracies 
+{'m0': 0.4061, 'm1': 0.5879, 'm2': 0.5928, 'm3': 0.5571}
+Mean subset ('m4',) accuracies : 0.535975
+Subset ('m0', 'm1') accuracies 
+{'m2': 0.8675, 'm3': 0.835, 'm4': 0.772}
+Mean subset ('m0', 'm1') accuracies : 0.8248333333333333
+Subset ('m0', 'm2') accuracies 
+{'m1': 0.8611, 'm3': 0.8278, 'm4': 0.7557}
+Mean subset ('m0', 'm2') accuracies : 0.8148666666666666
+Subset ('m0', 'm3') accuracies 
+{'m1': 0.8746, 'm2': 0.882, 'm4': 0.7687}
+Mean subset ('m0', 'm3') accuracies : 0.8417666666666667
+Subset ('m0', 'm4') accuracies 
+{'m1': 0.808, 'm2': 0.8054, 'm3': 0.7694}
+Mean subset ('m0', 'm4') accuracies : 0.7942666666666667
+Subset ('m1', 'm2') accuracies 
+{'m0': 0.5956, 'm3': 0.879, 'm4': 0.8133}
+Mean subset ('m1', 'm2') accuracies : 0.7626333333333334
+Subset ('m1', 'm3') accuracies 
+{'m0': 0.5964, 'm2': 0.9099, 'm4': 0.8091}
+Mean subset ('m1', 'm3') accuracies : 0.7717999999999999
+Subset ('m1', 'm4') accuracies 
+{'m0': 0.5584, 'm2': 0.8687, 'm3': 0.8348}
+Mean subset ('m1', 'm4') accuracies : 0.7539666666666666
+Subset ('m2', 'm3') accuracies 
+{'m0': 0.5883, 'm1': 0.9083, 'm4': 0.7942}
+Mean subset ('m2', 'm3') accuracies : 0.7636
+Subset ('m2', 'm4') accuracies 
+{'m0': 0.5488, 'm1': 0.8577, 'm3': 0.8256}
+Mean subset ('m2', 'm4') accuracies : 0.7440333333333333
+Subset ('m3', 'm4') accuracies 
+{'m0': 0.5669, 'm1': 0.8745, 'm2': 0.8845}
+Mean subset ('m3', 'm4') accuracies : 0.7753
+Subset ('m0', 'm1', 'm2') accuracies 
+{'m3': 0.9282, 'm4': 0.8263}
+Mean subset ('m0', 'm1', 'm2') accuracies : 0.8772500000000001
+Subset ('m0', 'm1', 'm3') accuracies 
+{'m2': 0.9454, 'm4': 0.8316}
+Mean subset ('m0', 'm1', 'm3') accuracies : 0.8885000000000001
+Subset ('m0', 'm1', 'm4') accuracies 
+{'m2': 0.9248, 'm3': 0.9037}
+Mean subset ('m0', 'm1', 'm4') accuracies : 0.91425
+Subset ('m0', 'm2', 'm3') accuracies 
+{'m1': 0.9501, 'm4': 0.8255}
+Mean subset ('m0', 'm2', 'm3') accuracies : 0.8877999999999999
+Subset ('m0', 'm2', 'm4') accuracies 
+{'m1': 0.9223, 'm3': 0.8991}
+Mean subset ('m0', 'm2', 'm4') accuracies : 0.9107000000000001
+Subset ('m0', 'm3', 'm4') accuracies 
+{'m1': 0.937, 'm2': 0.9376}
+Mean subset ('m0', 'm3', 'm4') accuracies : 0.9373
+Subset ('m1', 'm2', 'm3') accuracies 
+{'m0': 0.6131, 'm4': 0.8494}
+Mean subset ('m1', 'm2', 'm3') accuracies : 0.73125
+Subset ('m1', 'm2', 'm4') accuracies 
+{'m0': 0.6109, 'm3': 0.928}
+Mean subset ('m1', 'm2', 'm4') accuracies : 0.76945
+Subset ('m1', 'm3', 'm4') accuracies 
+{'m0': 0.6118, 'm2': 0.949}
+Mean subset ('m1', 'm3', 'm4') accuracies : 0.7804
+Subset ('m2', 'm3', 'm4') accuracies 
+{'m0': 0.6059, 'm1': 0.9495}
+Mean subset ('m2', 'm3', 'm4') accuracies : 0.7777000000000001
+Subset ('m0', 'm1', 'm2', 'm3') accuracies 
+{'m4': 0.8571}
+Mean subset ('m0', 'm1', 'm2', 'm3') accuracies : 0.8571
+Subset ('m0', 'm1', 'm2', 'm4') accuracies 
+{'m3': 0.9495}
+Mean subset ('m0', 'm1', 'm2', 'm4') accuracies : 0.9495
+Subset ('m0', 'm1', 'm3', 'm4') accuracies 
+{'m2': 0.964}
+Mean subset ('m0', 'm1', 'm3', 'm4') accuracies : 0.964
+Subset ('m0', 'm2', 'm3', 'm4') accuracies 
+{'m1': 0.9671}
+Mean subset ('m0', 'm2', 'm3', 'm4') accuracies : 0.9671
+Subset ('m1', 'm2', 'm3', 'm4') accuracies 
+{'m0': 0.6186}
+Mean subset ('m1', 'm2', 'm3', 'm4') accuracies : 0.6186
+Conditional accuracies for 1 modalities : 0.605505 +- 0.04158993087274851
+Conditional accuracies for 2 modalities : 0.7847066666666666 +- 0.03107100041303252
+Conditional accuracies for 3 modalities : 0.8474600000000001 +- 0.0704870867606259
+Conditional accuracies for 4 modalities : 0.87126 +- 0.13262359669380105
+Joint coherence : 0.0031999999191612005
+Uploading MVTCAE model to asenella/mmnistMVTCAE_config2_ repo in HF hub...
+Creating mmnistMVTCAE_config2_ in the HF hub since it does not exist...
+Successfully created mmnistMVTCAE_config2_ in the HF hub!