Now I am solving the problem of 3-class classification (in the task you need to understand who is in the picture - a panda, a cat or a dog). The dataset consists of 3000 images. To solve the problem, I use a slightly modified VGG architecture:
In the problem, it is required to rich >= 85% quality on validation set. To be honest, I have no any special thoughts yet. Can you please give advice on what to change / add / remove in the neural network architecture in order to achieve the desired result? I wan to note, that I am already using data augmentation.