TY  - BOOK
AU  - Xiao,Caide
ED  - Institute of Physics (Great Britain),
TI  - Mastering computer vision with PyTorch and machine learning
T2  - [IOP release $release]
SN  - 9780750362443
AV  - TA1634 .X535 2024eb
PY  - 2024///]
CY  - Bristol [England] (Temple Circus, Temple Way, Bristol BS1 6HG, UK)
PB  - IOP Publishing
KW  - PyTorch
KW  - Computer vision
KW  - Machine learning
KW  - bicssc
KW  - COMPUTERS / Data Science / Machine Learning
KW  - bisacsh
N1  - "Version: 20240401"--Title page verso; Includes bibliographical references; 1. Mathematical tools for computer vision -- 1.1. Probability, entropy and Kullback-Leibler divergence -- 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; 2. Image classifications by convolutional neural networks -- 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 -- 2.4. Transfer learning with ResNet for a dataset of Vegetable Images; 3. Image generation by GANs -- 3.1. The GAN theory -- 3.2. Applications of deep convolutional GANs -- 3.3. Conditional deep convolutional GANs; 4. Image generation by WGANs with gradient penalty -- 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; 5. Image generation by VAEs -- 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 &; 6. Image generation by infoGANs -- 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; 7. Object detection by YOLOv1/YOLOv3 models -- 7.1. Bounding boxes of Pascal VOC database for YOLOv1 -- 7.2. Encode VOC images with bounding boxes for YOLOv1 -- 7.3. ResNet18 model, IOU and a loss function -- 7.4. Utility functions for model training -- 7.5. Applications of YOLOv3 for real-time object detection; 8. YOLOv7, YOLOv8, YOLOv9 and YOLO-World -- 8.1. YOLOv7 for object detection for a custom dataset : MNIST4yolo -- 8.2. YOLOv7 for instance segmentation -- 8.3. Using YOLOv7 for human pose estimation (key point detection) -- 8.4. Applications of YOLOv8, YOLOv9 and YOLO-World Models; 9. U-Nets for image segmentation and diffusion models for image generation -- 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 -- 9.3. Using a pre-trained U-Net from Hugging Face to generate images -- 9.4. Generate photorealistic images from text prompts by stable diffusion; 10. Applications of vision transformers -- 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); 11. Knowledge distillation and its applications in DINO and SAM -- 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; 12. Applications of NeRF and 3D Gaussian splatting for synthesis of 3D scenes -- 12.1. Using MiDaS for image depth estimation -- 12.2. Neural Radiance Fields (NeRF) for synthesis of 3D scenes -- 12.3. Introduce 3D Gaussian splatting by 2D Gaussian splatting; Full-text restricted to subscribers or individual document purchasers; Students in computer science, engineering and physics; Also available in print
N2  - This book, together with the accompanying Python codes, provides a thorough and extensive guide for mastering advanced computer vision techniques for image processing by using the open-source machine learning framework PyTorch. Known for its user-friendly interface and Python programming style, PyTorch is accessible and one of the most popular tools among researchers and practitioners in the field of artificial intelligence
UR  - https://iopscience.iop.org/book/mono/978-0-7503-6244-3
ER  -