When training a CNN, what are the hyperparameters to tune first? I am training a convolutional neural network for object detection. Apart from the learning rate, what are the other hyperparameters that I should tune? And in what order of importance? Besides, I read that doing a grid search for hyperparameters is not the best way to go about training, and that random search is better in this case. Is random search really that good?
Firstly when you say an object detection CNN, there are a huge number of model architectures available. Considering that you have narrowed down on your model architecture a CNN will have a few common layers like the ones below with hyperparameters you can tweak:


Convolution Layer:- number of kernels, kernel size, stride length, padding
MaxPooling Layer:- kernel size, stride length, padding
Dense Layer:- size
Dropout:- Percentage to keep/drop