Modern Computer Vision with PyTorch
- Paperback: 824 pages
- Publisher: WOW! eBook (November 27, 2020)
- Language: English
- ISBN-10: 1839213477
- ISBN-13: 978-1839213472
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 PyTorch 1.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.
- Train a NN from scratch with NumPy and PyTorch
- Implement 2D and 3D multi-object detection and segmentation
- Generate digits and DeepFakes with autoencoders and advanced GANs
- Manipulate images using CycleGAN, Pix2PixGAN, StyleGAN2, and SRGAN
- Combine CV with NLP to perform OCR, image captioning, and object detection
- Combine CV with reinforcement learning to build agents that play pong and self-drive a car
- Deploy a deep learning model on the AWS server using FastAPI and Docker
- Implement over 35 NN architectures and common OpenCV utilities
By the end of this Modern Computer Vision with PyTorch book, you’ll be able to leverage modern NN architectures to solve over 50 real-world CV problems confidently.