Hands On Computer Vision With Detectron2
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Hands On Computer Vision With Detectron2
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Author : Van Vung Pham
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-04-14
Hands On Computer Vision With Detectron2 written by Van Vung Pham and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-14 with Computers categories.
Explore Detectron2 using cutting-edge models and learn all about implementing future computer vision applications in custom domains Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn how to tackle common computer vision tasks in modern businesses with Detectron2 Leverage Detectron2 performance tuning techniques to control the model's finest details Deploy Detectron2 models into production and develop Detectron2 models for mobile devices Book Description Computer vision is a crucial component of many modern businesses, including automobiles, robotics, and manufacturing, and its market is growing rapidly. This book helps you explore Detectron2, Facebook's next-gen library providing cutting-edge detection and segmentation algorithms. It's used in research and practical projects at Facebook to support computer vision tasks, and its models can be exported to TorchScript or ONNX for deployment. The book provides you with step-by-step guidance on using existing models in Detectron2 for computer vision tasks (object detection, instance segmentation, key-point detection, semantic detection, and panoptic segmentation). You'll get to grips with the theories and visualizations of Detectron2's architecture and learn how each module in Detectron2 works. As you advance, you'll build your practical skills by working on two real-life projects (preparing data, training models, fine-tuning models, and deployments) for object detection and instance segmentation tasks using Detectron2. Finally, you'll deploy Detectron2 models into production and develop Detectron2 applications for mobile devices. By the end of this deep learning book, you'll have gained sound theoretical knowledge and useful hands-on skills to help you solve advanced computer vision tasks using Detectron2. What you will learn Build computer vision applications using existing models in Detectron2 Grasp the concepts underlying Detectron2's architecture and components Develop real-life projects for object detection and object segmentation using Detectron2 Improve model accuracy using Detectron2's performance-tuning techniques Deploy Detectron2 models into server environments with ease Develop and deploy Detectron2 models into browser and mobile environments Who this book is for If you are a deep learning application developer, researcher, or software developer with some prior knowledge about deep learning, this book is for you to get started and develop deep learning models for computer vision applications. Even if you are an expert in computer vision and curious about the features of Detectron2, or you would like to learn some cutting-edge deep learning design patterns, you will find this book helpful. Some HTML, Android, and C++ programming skills are advantageous if you want to deploy computer vision applications using these platforms.
Object Detection And Segmentation Using Detectron2
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Author : Van Vung Pham
language : en
Publisher: Packt Publishing
Release Date : 2023-04-21
Object Detection And Segmentation Using Detectron2 written by Van Vung Pham and has been published by Packt Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-21 with categories.
Explore Detectron2 using cutting-edge models and learn all about implementing future computer vision applications in custom domains Purchase of the print or Kindle book includes a free PDF eBook Key Features: Learn how to tackle common computer vision tasks in modern businesses with Detectron2 Leverage Detectron2 performance tuning techniques to control the model's finest details Deploy Detectron2 models into production and develop Detectron2 models for mobile devices Book Description: Computer vision is a crucial component of many modern businesses, including automobiles, robotics, and manufacturing, and its market is growing rapidly. This book helps you explore Detectron2, Facebook's next-gen library providing cutting-edge detection and segmentation algorithms. It's used in research and practical projects at Facebook to support computer vision tasks, and its models can be exported to TorchScript or ONNX for deployment. The book provides you with step-by-step guidance on using existing models in Detectron2 for computer vision tasks (object detection, instance segmentation, key-point detection, semantic detection, and panoptic segmentation). You'll get to grips with the theories and visualizations of Detectron2's architecture and learn how each module in Detectron2 works. As you advance, you'll build your practical skills by working on two real-life projects (preparing data, training models, fine-tuning models, and deployments) for object detection and instance segmentation tasks using Detectron2. Finally, you'll deploy Detectron2 models into production and develop Detectron2 applications for mobile devices. By the end of this deep learning book, you'll have gained sound theoretical knowledge and useful hands-on skills to help you solve advanced computer vision tasks using Detectron2. What You Will Learn: Build computer vision applications using existing models in Detectron2 Grasp the concepts underlying Detectron2's architecture and components Develop real-life projects for object detection and object segmentation using Detectron2 Improve model accuracy using Detectron2's performance-tuning techniques Deploy Detectron2 models into server environments with ease Develop and deploy Detectron2 models into browser and mobile environments Who this book is for: If you are a deep learning application developer, researcher, or software developer with some prior knowledge about deep learning, this book is for you to get started and develop deep learning models for computer vision applications. Even if you are an expert in computer vision and curious about the features of Detectron2, or you would like to learn some cutting-edge deep learning design patterns, you will find this book helpful. Some HTML, Android, and C++ programming skills are advantageous if you want to deploy computer vision applications using these platforms.
Digital Technologies And Applications
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Author : Saad Motahhir
language : en
Publisher: Springer Nature
Release Date : 2024-08-31
Digital Technologies And Applications written by Saad Motahhir and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-31 with Computers categories.
This book presents volume 4 of selected research papers presented at the fourth International Conference on Digital Technologies and Applications (ICDTA’24). Highlighting the latest innovations in digital technologies as: artificial intelligence, Internet of Things, embedded systems, chatbot, network technology, digital transformation and their applications in several areas as Industry 4.0, sustainability, energy transition, and healthcare, the book encourages and inspires researchers, industry professionals, and policymakers to put these methods into practice.
Detectron2 In Practice
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Author : Richard Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-05-30
Detectron2 In Practice written by Richard Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-30 with Computers categories.
"Detectron2 in Practice" "Detectron2 in Practice" is a comprehensive guidebook for practitioners and researchers aiming to master the deployment and customization of Detectron2, Facebook AI's state-of-the-art computer vision library. The book begins by grounding readers in the foundational concepts of Detectron2, shedding light on its philosophy, modular architecture, and positioning within the broader landscape of deep learning frameworks. It provides a detailed analysis of supported tasks such as object detection, instance segmentation, keypoint detection, and panoptic segmentation, offering an informed comparison with leading alternatives like MMDetection and TensorFlow Object Detection API. Building on these foundations, the book offers an in-depth exploration of Detectron2's core architecture, APIs, and flexible training pipelines. Readers are guided through every step of the workflow, from dataset integration and annotation to advanced data augmentation, distributed training, and custom model development. Detailed chapters illuminate the intricacies of configuration systems, extensible trainer frameworks, data pipeline reengineering, and plugin integration, enabling users to design tailored solutions for research or production at scale. Practical advice on scaling workflows to the cloud, optimizing for diverse hardware, and deploying efficient models in real-world environments ensure that the book remains relevant for both enterprise and academic applications. In its final sections, "Detectron2 in Practice" transitions to applied use cases, benchmarking methodologies, and visionary perspectives on the evolution of computer vision. Real-world case studies spanning medical imaging, robotics, retail, and smart city applications highlight how Detectron2 empowers innovations across industries. The book concludes by surveying the future of the field, from the integration of vision transformers and self-supervised learning to best practices for community engagement and sustainability. With its thorough, example-driven approach, this volume establishes itself as an essential resource for any professional aiming to unlock the full potential of Detectron2 in contemporary computer vision tasks.
Mastering New Age Computer Vision
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Author : Zonunfeli Ralte
language : en
Publisher: BPB Publications
Release Date : 2025-02-19
Mastering New Age Computer Vision written by Zonunfeli Ralte and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-19 with Computers categories.
DESCRIPTION Mastering New Age Computer Vision is a comprehensive guide that explores the latest advancements in computer vision, a field that is enabling machines to not only see but also understand and interpret the visual world in increasingly sophisticated ways, guiding you from foundational concepts to practical applications. This book explores cutting-edge computer vision techniques, starting with zero-shot and few-shot learning, DETR, and DINO for object detection. It covers advanced segmentation models like Segment Anything and Vision Transformers, along with YOLO and CLIP. Using PyTorch, readers will learn image regression, multi-task learning, multi-instance learning, and deep metric learning. Hands-on coding examples, dataset preparation, and optimization techniques help apply these methods in real-world scenarios. Each chapter tackles key challenges, introduces architectural innovations, and improves performance in object detection, segmentation, and vision-language tasks. By the time you have turned the final page of this book, you will be a confident computer vision practitioner, armed with a comprehensive grasp of core principles and the ability to apply cutting-edge techniques to solve real-world problems. You will be prepared to develop innovative solutions across a broad spectrum of computer vision challenges, actively contributing to the ongoing advancements in this dynamic field. KEY FEATURES ● Master PyTorch for image processing, segmentation, and object detection. ● Explore advanced computer vision techniques like ViT and panoptic models. ● Apply multi-tasking, metric, bilinear pooling, and self-supervised learning in real-world scenarios. WHAT YOU WILL LEARN ● Use PyTorch for both basic and advanced image processing. ● Build object detection models using CNNs and modern frameworks. ● Apply multi-task and multi-instance learning to complex datasets. ● Develop segmentation models, including panoptic segmentation. ● Improve feature representation with metric learning and bilinear pooling. ● Explore transformers and self-supervised learning for computer vision. WHO THIS BOOK IS FOR This book is for data scientists, AI practitioners, and researchers with a basic understanding of Python programming and ML concepts. Familiarity with deep learning frameworks like PyTorch and foundational knowledge of computer vision will help readers fully grasp the advanced techniques discussed. TABLE OF CONTENTS 1. Evolution of New Age Computer Vision Models 2. Image Processing with PyTorch 3. Designing of Advanced Computer Vision Techniques 4. Designing Superior Computer Vision Techniques 5. Advanced Object Detection with FPN, RPN, and DetectoRS 6. Multi-instance Learning 7. More Advanced Multi-instance Learning 8. Beyond Classical Segmentation Panoptic Segmentation with SAM 9. Crafting Deep Metric Learning in Embedding Space 10. Navigating the Realm of Metric Learning 11. Multi-tasking with Multi-task Learning 12. Fine-grained Bilinear CNN 13. The Rise of Self-supervised Learning 14. Advancements in Computer Vision Landscape
Computer Vision Eccv 2022 Workshops
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Author : Leonid Karlinsky
language : en
Publisher: Springer Nature
Release Date : 2023-02-18
Computer Vision Eccv 2022 Workshops written by Leonid Karlinsky and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-18 with Computers categories.
The 8-volume set, comprising the LNCS books 13801 until 13809, constitutes the refereed proceedings of 38 out of the 60 workshops held at the 17th European Conference on Computer Vision, ECCV 2022. The conference took place in Tel Aviv, Israel, during October 23-27, 2022; the workshops were held hybrid or online. The 367 full papers included in this volume set were carefully reviewed and selected for inclusion in the ECCV 2022 workshop proceedings. They were organized in individual parts as follows: Part I: W01 - AI for Space; W02 - Vision for Art; W03 - Adversarial Robustness in the Real World; W04 - Autonomous Vehicle Vision Part II: W05 - Learning With Limited and Imperfect Data; W06 - Advances in Image Manipulation; Part III: W07 - Medical Computer Vision; W08 - Computer Vision for Metaverse; W09 - Self-Supervised Learning: What Is Next?; Part IV: W10 - Self-Supervised Learning for Next-Generation Industry-Level Autonomous Driving; W11 - ISIC Skin Image Analysis; W12 - Cross-Modal Human-Robot Interaction; W13 - Text in Everything; W14 - BioImage Computing; W15 - Visual Object-Oriented Learning Meets Interaction: Discovery, Representations, and Applications; W16 - AI for Creative Video Editing and Understanding; W17 - Visual Inductive Priors for Data-Efficient Deep Learning; W18 - Mobile Intelligent Photography and Imaging; Part V: W19 - People Analysis: From Face, Body and Fashion to 3D Virtual Avatars; W20 - Safe Artificial Intelligence for Automated Driving; W21 - Real-World Surveillance: Applications and Challenges; W22 - Affective Behavior Analysis In-the-Wild; Part VI: W23 - Visual Perception for Navigation in Human Environments: The JackRabbot Human Body Pose Dataset and Benchmark; W24 - Distributed Smart Cameras; W25 - Causality in Vision; W26 - In-Vehicle Sensing and Monitorization; W27 - Assistive Computer Vision and Robotics; W28 - Computational Aspects of Deep Learning; Part VII: W29 - Computer Vision for Civil and Infrastructure Engineering; W30 - AI-Enabled Medical Image Analysis: Digital Pathology and Radiology/COVID19; W31 - Compositional and Multimodal Perception; Part VIII: W32 - Uncertainty Quantification for Computer Vision; W33 - Recovering 6D Object Pose; W34 - Drawings and Abstract Imagery: Representation and Analysis; W35 - Sign Language Understanding; W36 - A Challenge for Out-of-Distribution Generalization in Computer Vision; W37 - Vision With Biased or Scarce Data; W38 - Visual Object Tracking Challenge.
Building Llms With Pytorch
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Author : Anand Trivedi
language : en
Publisher: BPB Publications
Release Date : 2025-03-13
Building Llms With Pytorch written by Anand Trivedi and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-13 with Computers categories.
DESCRIPTION PyTorch has become the go-to framework for building cutting-edge large language models (LLMs), enabling developers to harness the power of deep learning for natural language processing. This book serves as your practical guide to navigating the intricacies of PyTorch, empowering you to create your own LLMs from the ground up. You will begin by mastering PyTorch fundamentals, including tensors, autograd, and model creation, before diving into core neural network concepts like gradients, loss functions, and backpropagation. Progressing through regression and image classification with convolutional neural networks, you will then explore advanced image processing through object detection and segmentation. The book seamlessly transitions into NLP, covering RNNs, LSTMs, and attention mechanisms, culminating in the construction of Transformer-based LLMs, including a practical mini-GPT project. You will also get a strong understanding of generative models like VAEs and GANs. By the end of this book, you will possess the technical proficiency to build, train, and deploy sophisticated LLMs using PyTorch, equipping you to contribute to the rapidly evolving landscape of AI. WHAT YOU WILL LEARN ● Build and train PyTorch models for linear and logistic regression. ● Configure PyTorch environments and utilize GPU acceleration with CUDA. ● Construct CNNs for image classification and apply transfer learning techniques. ● Master PyTorch tensors, autograd, and build fundamental neural networks. ● Utilize SSD and YOLO for object detection and perform image segmentation. ● Develop RNNs and LSTMs for sequence modeling and text generation. ● Implement attention mechanisms and build Transformer-based language models. ● Create generative models using VAEs and GANs for diverse applications. ● Build and deploy your own mini-GPT language model, applying the acquired skills. WHO THIS BOOK IS FOR Software engineers, AI researchers, architects seeking AI insights, and professionals in finance, medical, engineering, and mathematics will find this book a comprehensive starting point, regardless of prior deep learning expertise. TABLE OF CONTENTS 1. Introduction to Deep Learning 2. Nuts and Bolts of AI with PyTorch 3. Introduction to Convolution Neural Network 4. Model Building with Custom Layers and PyTorch 2.0 5. Advances in Computer Vision: Transfer Learning and Object Detection 6. Advanced Object Detection and Segmentation 7. Mastering Object Detection with Detectron2 8. Introduction to RNNs and LSTMs 9. Understanding Text Processing and Generation in Machine Learning 10. Transformers Unleashed 11. Introduction to GANs: Building Blocks of Generative Models 12. Conditional GANs, Latent Spaces, and Diffusion Models 13. PyTorch 2.0: New Features, Efficient CUDA Usage, and Accelerated Model Training 14. Building Large Language Models from Scratch
Computer Vision Eccv 2022
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Author : Shai Avidan
language : en
Publisher: Springer Nature
Release Date : 2022-10-22
Computer Vision Eccv 2022 written by Shai Avidan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-10-22 with Computers categories.
The 39-volume set, comprising the LNCS books 13661 until 13699, constitutes the refereed proceedings of the 17th European Conference on Computer Vision, ECCV 2022, held in Tel Aviv, Israel, during October 23–27, 2022. The 1645 papers presented in these proceedings were carefully reviewed and selected from a total of 5804 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Computer Vision Eccv 2020
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Author : Andrea Vedaldi
language : en
Publisher: Springer Nature
Release Date : 2020-11-06
Computer Vision Eccv 2020 written by Andrea Vedaldi and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-06 with Computers categories.
The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.
Modern Computer Vision With Pytorch
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Author : V Kishore Ayyadevara
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-06-10
Modern Computer Vision With Pytorch written by V Kishore Ayyadevara and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-10 with Computers categories.
The definitive computer vision book is back, featuring the latest neural network architectures and an exploration of foundation and diffusion models Purchase of the print or Kindle book includes a free eBook in PDF format Key Features Understand the inner workings of various neural network architectures and their implementation, including image classification, object detection, segmentation, generative adversarial networks, transformers, and diffusion models Build solutions for real-world computer vision problems using PyTorch All the code files are available on GitHub and can be run on Google Colab Book DescriptionWhether you are a beginner or are looking to progress in your computer vision career, this book guides you through the fundamentals of neural networks (NNs) and PyTorch and how to implement state-of-the-art architectures for real-world tasks. The second edition of Modern Computer Vision with PyTorch is fully updated to explain and provide practical examples of the latest multimodal models, CLIP, and Stable Diffusion. You’ll discover best practices for working with images, tweaking hyperparameters, and moving models into production. As you progress, you'll implement various use cases for facial keypoint recognition, multi-object detection, segmentation, and human pose detection. This book provides a solid foundation in image generation as you explore different GAN architectures. You’ll leverage transformer-based architectures like ViT, TrOCR, BLIP2, and LayoutLM to perform various real-world tasks and build a diffusion model from scratch. Additionally, you’ll utilize foundation models' capabilities to perform zero-shot object detection and image segmentation. Finally, you’ll learn best practices for deploying a model to production. By the end of this deep learning book, you'll confidently leverage modern NN architectures to solve real-world computer vision problems.What you will learn Get to grips with various transformer-based architectures for computer vision, CLIP, Segment-Anything, and Stable Diffusion, and test their applications, such as in-painting and pose transfer Combine CV with NLP to perform OCR, key-value extraction from document images, visual question-answering, and generative AI tasks Implement multi-object detection and segmentation Leverage foundation models to perform object detection and segmentation without any training data points Learn best practices for moving a model to production Who this book is for This book is for beginners to PyTorch and intermediate-level machine learning practitioners who want to learn computer vision techniques using deep learning and PyTorch. It's useful for those just getting started with neural networks, as it will enable readers to learn from real-world use cases accompanied by notebooks on GitHub. Basic knowledge of the Python programming language and ML is all you need to get started with this book. For more experienced computer vision scientists, this book takes you through more advanced models in the latter part of the book.