Hi,
I’m working on an image classification problem, with 12 classes. I’d like to use test time augmentation by averaging the probabilities of each class over the augmented test images but I am uncertain as to the correct formula.
Currently, I just take the output of the final fc layer, treating it as unnormalized output, and apply a softmax across classes, interpreting the result as the probability of the class. This is what I average over, taking argmax after averaging.
The loss function is cross-entropy, which involves log(softmax) and so my question is: am I interpreting properly the probabilities in my scheme above, or should I do something different.
Grateful for any advice.