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)

Faster R-CNN – faster region proposal network-based CNN

We saw in the earlier section that Fast R-CNN brought down the time required for scoring (testing) images drastically, but the reduction ignored the time required for generating Region Proposals, which use a separate mechanism (though pulling from the convolution map from CNN) and continue proving a bottleneck. Also, we observed that though all three challenges were resolved using the common features from convolution-map in Fast R-CNN, they were using different mechanisms/models.

Faster R-CNN improves upon these drawbacks and proposes the concept of Region Proposal Networks (RPNs), bringing down the scoring (testing) time to 0.2 seconds per image, even including time for Region Proposals.


Fast R-CNN was doing the scoring (testing) in 0.3 seconds per image, that too excluding the time required for the process equivalent to Region Proposal.

Faster R-CNN: Working - The Region Proposal Networking acting as Attention Mechanism

As shown in...