Computer Vision Projects with Pytorch: Design and Develop Production-Grade Models

$49.99

SKU: 9781484282724
Author: Kulkarni, Akshay Shivananda, Adarsha Sharma, Nitin Ranjan
Publication Date: 07/25/2022
Publisher: Apress
Binding: Paperback
Media: Book
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Description

Chapter 1: Building Blocks of Computer VisionChapter Goal: The chapter will start with the basic concepts of Computer Vision. We will cover theoretical aspects that lays the foundation for the upcoming hands-on projects on Computer Vision. No of pages:35Sub -Topics1. Overview of Computer Vision2. Understanding AlexNET, Convolutional Neural Network and receptive fields3. Understanding advanced concepts like RESNETS and inception network4. Discuss how usage of batch normalization, drop outs, data augmentation techniques help solve data insufficiency in deep learning models5. Introduction to PyTorch for Computer Vision models
Chapter 2: Building Image Classification ModelChapter Goal: The chapter will discuss about image classification model along with data augmentation techniques.No of pages: 40Sub – Topics 1. Data preparation for image classification problem2. Data augmentation techniques3. Setting up model architecture with explanation4. Train and run inference for the Image Classification model5. Discuss Grouped Convolution, Dilated Convolution and transposed convolution and their application
Chapter 3: Building Object Detection ModelChapter Goal: This chapter will explain the core difference between simple classification model to detecting objects in an image. We will understand optimizing loss function to get the final object localized and detected. The chapter will take through some concepts of the existing models and how to fine tune them.No of pages: 30Sub – Topics: 1. Exploring Object Detection concepts like FastRCNN, YOLO2. Explaining annotations and examples of how annotations are used in Object Detection3. Explaining loss function components4. Building Object Detection model, using transfer learning technique5. Running inference on fine-tuned model
Chapter 4: Building Image Segmentation ModelChapter Goal: The chapter will define how single or multiple images can be segmented in an image. How a user can define a loss function and develop a model to segregate image outlines. No of pages: 35Sub – Topics: 1. Concepts on how segmentation works on Images2. Explaining custom pre trained models3. Defining and explaining loss functions4. Implementing & fine-tuning Image Segmentation model
Chapter 5: Image Similarity & Image based SearchChapter Goal: The chapter deals with the explanation of how the image similarity works and how use cases move around this concept. No of pages: 25Sub – Topics: 1. Defining Image similarity and anomaly problems for images2. Defining the datasets3. Defining the loss functions and methodologies4. Providing solutions for Detecting Image similarities
Chapter 6: Image Anomaly DetectionChapter Goal: The chapter deals with the explanation of how anomalies from images can be detected and use-cases around it.No of pages: 20Sub – Topics: 1. Defining anomaly problems for images2. Defining the datasets3. Defining the loss functions and methodologies4. Detecting anomalies on images
Chapter 7: Video Processing Applications using PyTorchChapter Goal: This chapter deals with various mechanism of video processing techniques. This chapter will help one to deal with untangling the complexities of video with series of images placed in time sequence. Concepts of RNN/LSTM/GRU will be discussed t