@sml0820: Thanks for sharing some suggestions. I'd love to hear more about your approach to get into the top hundred? Are you currently using the pre-trained VGG model wth dropout? Are you using data augmentation, if so, what settings are you using? I explored through many of the options for data augmentation and built a new training image set with these settings:
aug_gen = image.ImageDataGenerator(rotation_range=15,
My current best score (test loss ~ 1.2) is a fresh (non-vgg) CNN based on the materials presented in Lesson 4 and trained on an augmented data set of 6400 images produced using the settings above.
Also curious if you've incorporated the extra info @Gelu74 mentioned (image size). I've shied away from this since its likely they'll resolve this in the next test set, but curious if you've used that data to improve your model.