  Transfer learning is one of the subjects undergoing intense study in the area
of machine learning. In object recognition and object detection there are known
experiments for the transferability of parameters, but not for neural networks
which are suitable for object detection in real time embedded applications,
such as the SqueezeDet neural network. We use transfer learning to accelerate
the training of SqueezeDet to a new group of classes. Also, experiments are
conducted to study the transferability and co-adaptation phenomena introduced
by the transfer learning process. To accelerate training, we propose a new
implementation of the SqueezeDet training which provides a faster pipeline for
data processing and achieves 1.8 times speedup compared to the initial
implementation. Finally, we created a mechanism for automatic hyperparameter
optimization using an empirical method.
