Taking a long shot here. So I have a a small dataset of ~500 images with discrete labels from 1 to 9. My task is to detect the per-class and overall accuracy of this classification method using a (deep) neural network.
I get an accuracy of 56% using multinomial logistic regression using train/test split method. I used transfer learning in PyTorch for some of the categories that have >25 images (2, 3, 5, 7, and 9). However, I do not know how to use this tutorial and report test accuracy when I only have like 3 or 11 images in a category.
Here is the number of images per category:
category 1: 3 category 2: 66 category 3: 175 category 4: 18 category 5: 54 category 6: 12 category 7: 91 category 8: 11 category 9: 38
What (deep) neural network method (hopefully with a link to tutorial, would you suggest that could work with a dataset like mine that has severe data imbalance and also some categories are having <10 images?
I have provided further discussion of the problem I am dealing with as well here: https://discuss.pytorch.org/t/leave-one-out-cross-validation-for-images-with-labels-and-discrete-confidence-levels-for-labels/29227 Here is the confusion matrix I get from multinomial logistic regression: