Hi Oren,
Thanks for the advises. I’ll train on a few classes for the experimentation purposes with all the training images for those classes. As I do not have any specific knowledge about the food images available in the dataset, for determining the subset of classes maybe I’ll fine tune a pre-trained resnet34 for one epoch and plot the most confused classes to find some hard classes.
Also, I may be completely wrong but I don’t know why I feel like as we go from the toy dataset to the full dataset we may have to switch to a deeper architecture (e.g from resnet34 to resnet50). I was just wondering if you can provide some clarification on this because if this is the case then we have find a new set of hyperparameters again for the new architecture, right? And if that is the case then I’m not sure what purpose would the toy dataset serve?
Have not started working on this yet but will surely share after I’m done. 