The process for training a CNN for classifying images consists of the following steps −

Data Preparation In this step, we center-crop the images and resize them so that all images for training and testing would be of the same size. This is usually done by running a small Python script on the image data.

Model Definition In this step, we define a CNN architecture. The configuration is stored in .pb (protobuf) file.

Solver Definition In this, we define the solver configuration file. The solver does the model optimization.

Model Training In this, we use the built-in Caffe utility to train the model. The training may take a considerable amount of time and CPU usage. After the training is completed, Caffe stores the model in a file, which can, later on, be used on test data and final deployment for predictions.