This question came up during class. There is no direct answer. It depends on domain / data / use case / number of classes. Guidelines of recommendations are
- Number of Layers of convolution, Experiment with VGG16 / 19 to get started
- Balacing imbalanced datasets
- Depth of Layers - 32/ 64/ 256 as needed
- Relu, Experiment, Customize on activation functions
- Adjusting learning rates / Loss functions
- Early Stopping / Dropouts for regularization
- Domain Relevant Augmentation
- Transfer Learning Approach
Keep Thinking!!!
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