Hi all,
I’m trying to solve a problem of video recognition using 3d cnn’s.
I want to classify the videos into 6 classes, I tried training an END-TO-END 3d cnn’s model that didn’t give me good results (around 40% accuracy) so I decided to try a different approach and training 6 models of binary classification for each class separately.
Each individual model out of the 6 models I trained gave me good accuracy and low loss.
Now I want to ensemble all the 6 models together but the problem is that each of the 6 models has it’s own mean and std normalization.
My question is: I want to be able to load a batch of videos and to normalize them according to the model they are about to be processed with - I have 6 models so I need to normalize the videos 6 times.
What is the best way for doing that? Do I need to create a different dataset for each of the models and normalize them accordingly or is there a much more simple and efficient way?
Best regards,
Yana