OpenCV 4 with Python Blueprints - Second Edition

Preparing the model

First, let's think about how we can make a localizer using the output of the base model.

As mentioned previously, the output tensor of the base model has a shape of (None, 7, 7, 1280). The output tensor represents features obtained using a convolutional network. We can suppose that some spatial information is encoded in the spatial indexes (7,7).

Let's try to reduce the dimensionality of our feature map using a couple of convolutional layers and create a regressor that should predict the corner coordinates of the pets' head bounding boxes provided by the dataset.

Our convolutional layers will have several options that are the same:

conv_opts = dict(    activation='relu',    padding='same',    kernel_regularizer="l2")

First of ...