In this chapter, we'll continue our discussion on the recent advances in image processing with deep learning. We will be dealing with a few problems in particular, and shall try to solve them using deep learning with deep CNNs. 

We will look at the object detection problem, understanding the basic concepts involved, then examine how to write code to solve the problem with object proposals and a You Only Look On (YOLO) v2 pre-trained deep neural network in Keras. You will be provided with resources that will help you in training the YOLO net.

Get ready to learn about transfer learning and solve deep segmentation problems using the DeepLab library. You will learn to specify which layers to train while training a deep learning model, and demonstrate a custom image classification problem by only learning the weights for the FC layers of a VGG16 network.

You may be surprised to learn how deep learning can be used in art generation, with deep style transfer models, where the content of one image and the style of another image can be used to obtain a final image.

 The topics to be covered in this chapter are the following:

• A fully convolutional model for detecting objects: YOLO (v2)
• Deep segmentation with DeepLab (v3)
• Transfer learning: what is it and when to use it
• Deep style transfer with cv2 using a pretrained torch-based deep learning model