Book Image

Practical Convolutional Neural Networks

By : Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari
Book Image

Practical Convolutional Neural Networks

By: Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari

Overview of this book

Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models. This book starts with an overview of deep neural networkswith the example of image classification and walks you through building your first CNN for human face detector. We will learn to use concepts like transfer learning with CNN, and Auto-Encoders to build very powerful models, even when not much of supervised training data of labeled images is available. Later we build upon the learning achieved to build advanced vision related algorithms for object detection, instance segmentation, generative adversarial networks, image captioning, attention mechanisms for vision, and recurrent models for vision. By the end of this book, you should be ready to implement advanced, effective and efficient CNN models at your professional project or personal initiatives by working on complex image and video datasets.
Table of Contents (11 chapters)

Mask R-CNN – Instance segmentation with CNN

Faster R-CNN is state-of-the-art stuff in object detection today. But there are problems overlapping the area of object detection that Faster R-CNN cannot solve effectively, which is where Mask R-CNN, an evolution of Faster R-CNN can help.

This section introduces the concept of instance segmentation, which is a combination of the standard object detection problem as described in this chapter, and the challenge of semantic segmentation.


In semantic segmentation, as applied to images, the goal is to classify each pixel into a fixed set of categories without differentiating object instances.

Remember our example of counting the number of dogs in the image in the intuition section? We were able to count the number of dogs easily, because they were very much apart, with no overlap, so essentially just counting the number of objects did the job. Now, take the following image, for instance, and count the number of tomatoes using object detection. It...