Summary

In this chapter, we discussed a few advanced deep learning applications to solve a few complex image processing problems. We started with basic concepts in image classification with localization and object detection. Then we demonstrated how a popular YOLO v2 FCN pre-trained model can be used to detect objects in images and draw boxes around them. Next, we discussed the basic concepts in semantic segmentation and then demonstrated how to use DeepLab v3+ (along with a summary on its architecture) to perform semantic segmentation of an image. Then we defined transfer learning and explained how and when it is useful in deep learning, along with a demonstration on transfer learning in Keras to classify flowers with a pre-trained VGG16 model. Finally, we discussed how to generate novel artistic images with deep neural style transfer, and demonstrated this with Python and OpenCV and a pre-trained Torch model. You should be familiar with how to use pre-trained deep learning models to solve...