Description
Chapter 1: Building Blocks of Computer Vision Chapter 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 :35 Sub -Topics 1. Overview of Computer Vision 2. Understanding AlexNET, Convolutional Neural Network and receptive fields 3. Understanding advanced concepts like RESNETS and inception network 4. Discuss how usage of batch normalization, drop outs, data augmentation techniques help solve data insufficiency in deep learning models 5. Introduction to PyTorch for Computer Vision models Chapter 2: Building Image Classification Model Chapter Goal: The chapter will discuss about image classification model along with data augmentation techniques. No of pages: 40 Sub – Topics 1. Data preparation for image classification problem 2. Data augmentation techniques 3. Setting up model architecture with explanation 4. Train and run inference for the Image Classification model 5. Discuss Grouped Convolution, Dilated Convolution and transposed convolution and their application Chapter 3: Building Object Detection Model Chapter 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: 30 Sub – Topics: 1. Exploring Object Detection concepts like FastRCNN, YOLO 2. Explaining annotations and examples of how annotations are used in Object Detection 3. Explaining loss function components 4. Building Object Detection model, using transfer learning technique 5. Running inference on fine-tuned model Chapter 4: Building Image Segmentation Model Chapter 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: 35 Sub – Topics: 1. Concepts on how segmentation works on Images 2. Explaining custom pre trained models 3. Defining and explaining loss functions 4. Implementing & fine-tuning Image Segmentation model Chapter 5: Image Similarity & Image based Search Chapter Goal: The chapter deals with the explanation of how the image similarity works and how use cases move around this concept. No of pages: 25 Sub – Topics: 1. Defining Image similarity and anomaly problems for images 2. Defining the datasets 3. Defining the loss functions and methodologies 4. Providing solutions for Detecting Image similarities Chapter 6: Image Anomaly Detection Chapter Goal: The chapter deals with the explanation of how anomalies from images can be detected and use-cases around it. No of pages: 20 Sub – Topics: 1. Defining anomaly problems for images 2. Defining the datasets 3. Defining the loss functions and methodologies 4. Detecting anomalies on images Chapter 7: Video Processing Applications using PyTorch Chapter 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 to solve real time use-cases on videos. No of pages: 50 Sub – Topics: 1. Setting up concepts of time dependent feature set 2. Extrapolating images to videos 3. Setting up concepts for video processing using Convolutional Neural Networks 4. Defining the dataset and the loss function 5. Defining the model 6. Training the model and run inference Chapter 8: Super-resolution through Upscaling & GAN Chapter Goal: This chapter deals with foundations on Generative Adversarial Networks in the field of computer vision. The concepts will be extrapolated with an use-case to how it is being used in super resolution (Enhancing Image Quality) No of pages: 30 Sub – Topics: 1. Establish the concept of upscaling in images 1. Foundations of VAE and GAN in images 2. Setting up codes in GAN for super resolution 3. Using the concept to understand data augmentation using GAN Chapter 9: Body Posture Detection Chapter Goal: This chapter will establish the concept of multiple body posture detection. It will have the code encompassed the detection and multiple methods around posture detection applications. No of pages: 30 Sub – Topics: 1. Discussing top-down and bottom-up approach to detect persons 2. Discuss open pose detection model to establish body pose 3. Use of segmentation technique to detect body pose Chapter 10: Explainable AI for Computer Vision using GRADCAM Chapter Goal: This chapter deals with foundations on how a deep learning model results can be explained. An overview of GRADCAM and how the concepts help someone explaining a Computer Vision model will be discussed in abundance. No of pages: 15 Sub – Topics: 1. Revisit the concepts of explain-able AI 2. Deep learning explainers to CV classification model 3. Setting up concepts of GRADCAM 4. Implementing how Computer Vision models can be interpreted by GRADCAM
