I've a dataset with about 123 images (two categories, 19 defect and 104 no defect).
I've to implement a classifier so I've decided to split my data in train (70% of all data), validation (20% of all data) and test (10% of all data) set.
At the end I've:
- train: 72 no defet + 13 defect
- validation: 20 no defect + 3 defect
- test: 12 no defect + 3 defect
For the training set I've decided to perform data augmentation in order to increase the number of samples. Having validation and test set so much small with few samples for defect could be a problem? And if yes could I improve this situation in some way?