Can I retrain my keras model on small dataset with some empty folders I am working on a vehicle classification problem. 

I trained my model with 605 labels(folders) and 300k photos.

After testing my trained model, I checked my wrong answers' training and validation photos. I realized some of my labels training or validation photos are wrong or not enough different from another. 

I can prepare a small dataset that only contains these wrong learnt labels photos and keep other labels folders empty. 

If i retrain my model, will I get better accuracy? Or should I train from scratch?
What you are suggesting is to fine tune the network giving it a portion of the starting data set? 

If yes, well you can try, but I am not sure what the outcome would be. But remember to keep a train and test set separated!