Facial Expression Action Units Emotions Recognition - project

[alessa (Aless Bandrabur)]

@TheShadow29

note is that the weights in the WeightedRandomSampler correspond to the weights of each of the indices and not the classes.

You could get balanced dataset in a much easier way using StratifiedSampler found here:https://github.com/ncullen93/torchsample/blob/master/torchsample/samplers.py#L22. I found this following the thread here: How to enable the dataloader to sample from each class with equal probability - PyTorch Forums which would be exactly what you need (I guess?).

Additionally, you could also give more penalty to some of the classes as well which are under-represented. For that, you would need to pass the weights in the critic function of the leaner object and it would look something like learn.crit = nn.NLLLoss(weights=weights).

Thank you Arka for this information. After managing the samplers and understanding them finally I created a script for generating cvs with any linear combinations with any dimension of the AUs. For example just AU6 and AU12 - 2000 each, or 200 each, and so on.

Currently I work on a basic task of happy/non-happy based on action units so I created a cvs with two classes if the pictures were annotated with (Au6, Au12) or if they were missing.

The results are quite nice - just the model doesn’t generalize very well I guess - because when I test it with pictures from the lab it is a mess.

epoch      trn_loss   val_loss   accuracy                   
    x      0.044441   0.107906   0.9625    

Next step is to try to make it generalize better since I reached it already the overfitting on the training data, by the following steps