This is my first post. I just finished Lesson 1 and in one moment Jeremy mentions how some students downloaded 2x10 images of other things and made predictions for those things.
I created a folder /vehicles next to /dogcats and in there I have /vehicles/train/cars/[list] /vehicles/train/bikes/[list] and then /vehicles/valid/cars/[list] /vehicles/valid/bikes/[list].
Train folders each have 10 images of cars and 10 images of bikes and in VALID folder I have 4 images of cars and 4 images of bikes.
My prediction is mixed up and the percentage is about 10%.
I am familiar with concepts of cross validation sets but I cannot seem to apply in this particular case.
How can I retrain the model to recognize cars/bikes ?