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)

Fast R-CNN – fast region-based CNN

Fast R-CNN, or Fast Region-based CNN method, is an improvement over the previously covered R-CNN. To be precise about the improvement statistics, as compared to R-CNN, it is:

  • 9x faster in training
  • 213x faster at scoring/servicing/testing (0.3s per image processing), ignoring the time spent on region proposals
  • Has higher mAP of 66% on the PASCAL VOC 2012 dataset

Where R-CNN uses a smaller (five-layer) CNN, Fast R-CNN uses the deeper VGG16 network, which accounts for its improved accuracy. Also, R-CNN is slow because it performs a ConvNet forward pass for each object proposal without sharing computation:

Fast R-CNN: Working

In Fast R-CNN, the deep VGG16 CNN provides essential computations for all the stages, namely:

  • Region of Interest (RoI) computation
  • Classification Objects (or background) for the region contents
  • Regression for enhancing the bounding box

The input to the CNN, in this case, is not raw (candidate) regions from the image, but the (complete) actual image...