Book Image

Modern Computer Vision with PyTorch

By : V Kishore Ayyadevara, Yeshwanth Reddy
5 (1)
Book Image

Modern Computer Vision with PyTorch

5 (1)
By: V Kishore Ayyadevara, Yeshwanth Reddy

Overview of this book

Deep learning is the driving force behind many recent advances in various computer vision (CV) applications. This book takes a hands-on approach to help you to solve over 50 CV problems using PyTorch1.x on real-world datasets. You’ll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. You’ll then perform image classification using convolutional neural networks and transfer learning and understand how they work. As you progress, you’ll implement multiple use cases of 2D and 3D multi-object detection, segmentation, human-pose-estimation by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform. The book will also guide you in performing facial expression swapping, generating new faces, and manipulating facial expressions as you explore autoencoders and modern generative adversarial networks. You’ll learn how to combine CV with NLP techniques, such as LSTM and transformer, and RL techniques, such as Deep Q-learning, to implement OCR, image captioning, object detection, and a self-driving car agent. Finally, you'll move your NN model to production on the AWS Cloud. By the end of this book, you’ll be able to leverage modern NN architectures to solve over 50 real-world CV problems confidently.
Table of Contents (25 chapters)
Section 1 - Fundamentals of Deep Learning for Computer Vision
Section 2 - Object Classification and Detection
Section 3 - Image Manipulation
Section 4 - Combining Computer Vision with Other Techniques

Working details of SSD

So far, we have seen a scenario where we made predictions after gradually convolving and pooling the output from the previous layer. However, we know that different layers have different receptive fields to the original image. For example, the initial layers have a smaller receptive field when compared to the final layers, which have a larger receptive field. Here, we will learn how SSD leverages this phenomenon to come up with a prediction of bounding boxes for images.

The workings behind how SSD helps overcome the issue of detecting objects with different scales is as follows:

  • We leverage the pre-trained VGG network and extend it with a few additional layers until we obtain a 1 x 1 block.
  • Instead of leveraging only the final layer for bounding box and class predictions, we will leverage all of the last few layers to make class and bounding box predictions.
  • In place of anchor boxes, we will come up with default boxes that have a specific set of scale and aspect...