Book Image

Modern Computer Vision with PyTorch

By : V Kishore Ayyadevara, Yeshwanth Reddy
5 (2)
Book Image

Modern Computer Vision with PyTorch

5 (2)
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


  1. What happens if the learning rate of generator and discriminator models is high?
  2. In a scenario where the generator and discriminator are very well trained, what is the probability of a given image being real?
  3. Why do we use convtranspose2d in generating images?
  4. Why do we have embeddings with a high embedding size compared with the number of classes in conditional GANs?
  5. How can we generate images of men who have a beard?
  6. Why do we have Tanh activation in the last layer in the generator and not ReLU or Sigmoid?
  7. Why did we get realistic images even though we did not denormalize the generated data?
  1. What happens if we do not crop faces corresponding to images prior to training the GAN?
  2. Why do the weights of the discriminator not get updated when training the generator (as the generator_train_step function involves the discriminator network)?
  3. Why do we fetch losses on both real and fake images while training the discriminator, but only the loss on fake images while training the...