Mastering Computer Vision with PyTorch and Machine Learning 2024

What this book is about
	Prerequisites to readers
	Structure of the book
	Keywords
	Acknowledgements
	Author biography
	Dr Caide Xiao
	Chapter 1
	1.1 Probability, entropy and Kullback-Leibler divergence
	1.1.1 Probability and Shannon entropy
	1.1.2 Kullback-Leibler divergence and cross entropy
	1.1.3 Conditional probability and joint entropies
	1.1.4 Jensen’s inequality
	1.1.5 Maximum likelihood estimation and over fitting
	1.1.6 Application of expectation-maximization algorithm to find a PDF
	1.2 Using a gradient descent algorithm for linear regression
	1.3 Automatic gradient calculations and learning rate schedulers
	1.4 Dataset, dataloader, GPU and models saving
	1.5 Activation functions for nonlinear regressions
	References
	Chapter 2
	2.1 Classification of hand written digits in the MNIST database
	2.2 Mathematical operations of a 2D convolution
	2.3 Using ResNet9 for CIFAR-10 classification
	References
	Chapter 3
	3.1 The GAN theory
	3.1.1 Implement a GAN for quadratic curve generation
	3.1.2 Using a GAN with two fully connected layers to generate MINST Images
	3.2 Applications of deep convolutional GANs
	3.2.1 Mathematical operations of ConvTranspose2D
	3.2.2 Applications of a DCGAN for MNIST and fashion MNIST
	3.2.3 Using a DCGAN to generate fake anime-faces and fake CelebA images
	3.3 Conditional deep convolutional GANs
	3.3.1 Applications of a cDCGAN to MNIST and fashion MNIST datasets
	3.3.2 Applications of a cDCGAN to generate fake Rock Paper Scissors images
	References
	Chapter 4
	4.1 Using a WGAN or a WGAN-GP for generation of fake quadratic curves
	4.2 Using a WGAN-GP for Fashion MNIST
	4.3 WGAN-GP for CelebA dataset and Anime Face dataset
	4.4 Implementation of a cWGAN-GP for Rock Paper Scissors dataset
	References
	Chapter 5
	5.1 VAE and beta-VAE
	5.2 Application of beta-VAE for fake quadratic curves
	5.3 Application of beta-VAE for the MNIST dataset
	5.4 Using VAE-GAN for MNIST, Fashion MNIST & Anime-Face Dataset
	References
	Chapter 6
	6.1 Using infoGAN to generate quadratic curves
	6.2 Implementation of infoGAN for the MNIST dataset
	6.3 infoGAN for fake Anime-face dataset images
	6.4 Implementation of infoGAN to the rock paper scissors dataset
	Reference
	Chapter 7
	7.1 Bounding boxes of Pascal VOC database for YOLOv1
	7.2 Encode VOC images with bounding boxes for YOLOv1
	7.2.1 VOC image augmentations with bounding boxes
	7.2.2 Encoding bounding boxes to grid cells for YOLOv1 model training
	7.2.3 Chess pieces dataset from Roboflow
	7.3 ResNet18 model, IOU and a loss function
	7.3.1 Using ResNet18 to replace YOLOv1 model
	7.3.2 Intersection over union (IOU) and the loss function
	7.4 Utility functions for model training
	7.5 Applications of YOLOv3 for real-time object detection
	References
	Chapter 8
	8.1	YOLOv7 for object detection for a custom dataset: MNIST4yolo
	8.2	YOLOv7 for instance segmentation
	8.4 Applications of YOLOv8, YOLOv9 and YOLO-World models
	8.4.1 Image object detection, segmentation, classification and pose estimation
		#	The End
	8.4.3 Car tracking and counting for a video file
	References
	Chapter 9
	9.1 Retinal vessel segmentation by a U-Net for DRIVE dataset
	9.2 Using an attention U-Net diffusion model for quadratic curve generation
	9.2.1 The forward process in a DDPM
	9.2.2 The backward process in the DDPM
	9.3 Using a pre-trained U-Net from Hugging Face to generate images
	9.4 Generate photorealistic images from text prompts by stable diffusion
	References
	Chapter 10
	10.1 The architecture of a basic ViT model
	10.2	Hugging Face ViT for CIFAR10 image classification
	10.3	Zero shot image classification by OpenAI CLIP
	10.4 Zero shot object detection by Hugging Face’s OWL-ViT
	10.5 RT-DETR (a vision transformers-based real-time object detector)
	References
	Chapter 11
	11.1 Knowledge distillation for neural network compression
	11.2 DINO: emerging properties in self-supervised vision transformers
	11.3 DINOv2 for image retrieval, classification and feature visualization
	11.4 Segment anything model: SAM and FastSAM
	References
	Chapter 12
	12.1 Using MiDaS for image depth estimation
	12.2 Neural Radiance Fields (NeRF) for synthesis of 3D scenes
	12.2.1 Camera intrinsic and extrinsic matrices
	12.2.2 Using MLP with Gaussian Fourier feature mapping to reconstruct images
	12.2.3 The physics principle of render volume density in NeRF
	12.3 Introduce 3D Gaussian splatting by 2D Gaussian splatting
	References
	A Kullback-Leibler divergence of two multivariate normal distributions
	B Expectation-maximization algorithm
	C Gradients of MSE loss function to weights in a linear regression
	D Application of a VAE-GAN to generate fake Anime-faces dataset images
	E Applications of a cWGAN-GP system to MNIST or fashion MNIST
	F Four applications of pre-trained Detectron2 models
	G Traffic tracking and counting for objects in multiple COCO classes
	H U-Net Wasserstein generative adversarial networks for retina
	I DDPM forward process posterior distribution and Lvlb
	J An Improved Version of Project 11.3.1 to Avoid a FAISS Issue
	K Tiny NeRF codes for lego 3D scene synthesis