I am trying to understand what am I doing wrong. I have several multi label data sets, all of which have the same structure on the local file system, which is compatible with torch’ .e.g.:
For the Mnist data set which has 10 different labels and hance 10 folders:
For the ucf101 data set which has 102 different labels and hence 102 folders:
For reference the code for this post is here:
When using the code on CIFAR 10, which is downloaded automatically, e.g.:
Learning works great and both loss and accuracy get better over time.
However for Mnist/ucf101/ any other data set which I store locally, learning does not happen at all.
- Is it true that the default torch ImageLoader takes care of the labels for the classification without the need to one hot encode the labels? I am using CrossEntropyLoss and returning FC in forward:
- In case I am doing it right, where is my mistake in the code? Why CIFAR works well? while local data sets which confirm to the multi label folder convension do not work?