Mastering computer vision with PyTorch and machine learning /

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"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 regressions2. 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 Images3. Image generation by GANs -- 3.1. The GAN theory -- 3.2. Applications of deep convolutional GANs -- 3.3. Conditional deep convolutional GANs4. 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 dataset5. 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 dataset7. 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 detection8. 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 Models9. 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 diffusion10. 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 FastSAM12. 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.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.Students in computer science, engineering and physics.Also available in print.Mode of access: World Wide Web.System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.Dr. Caide Xiao was born in China in and obtained his bachelor's degree in physics from Centre China Normal University in 1979 and was a lecturer of medical physics in Yunyang University. Following his PhD in optical biosensors from Tsinghua University he has subsequently been a research fellow and visiting scholar at the Biotechnology Research Institute in Montreal, Oakland University in Rochester, West Virginia University and the University of Calgary.Title from PDF title page (viewed on May 1, 2024).

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: .,

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1 online resource (various pagings) :illustrations (some color).

Language

English

ISBN/ISSN

9780750362443

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006.3/7

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Subject(s)

Machine learning.
Computer vision.
COMPUTERS / Data Science / Machine Learning.

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Caide Xiao.

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