Is it possible to design a deep CNN model on a small size image dataset I have started a project of classifying a dataset using deep learning. I have tried transfer learning on pretrained models. Now, I want to design a CNN model which can do this work of classification but everywhere it says it needs large data. My question is: Is it possible to design a deep CNN model on an image dataset of size less than 1000 images?
According to your description of the data, it is highly probable that training any neural network of reasonable size will overfit the training data.

One option is to apply any possible regularization approach; these come to mind:


Use L1/L2 weight regularization.
Reduce the number of parameters as much as you can (less layers, less number of filters).
Use dropout.


Also, try any data augmentation trick: rotate images, mirror images, crop and scale images, etc.