Building Computer Vision Projects with OpenCV 4 and C++

Preparing a model for OpenCV

TensorFlow generates multiple files when we train a new model, generating files for events that store accuracy and loss, and other metrics obtained in each step; also, some files will store the variable results obtained for each step or checkpoint. These variables are the weights that networks learn in training. But it's not convenient to share all these files in production, as OpenCV would not be able to manage them. At the same time, there are nodes that are used only for training and not for inference. We have to remove these nodes from the model, nodes such as dropouts layers or training input iterators.

To put our model into production, we need to do the following:

• Freeze our graph
• Remove nodes/layers that ...