Object Detection And Segmentation Using 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.

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.

Detectron2 Unlocked A Hands On Guide To Object Detection Instance Segmentation And Production Deployment

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Author : William E Clark
language : en
Publisher: Walzone Press
Release Date : 2025-11-08

Detectron2 Unlocked A Hands On Guide To Object Detection Instance Segmentation And Production Deployment written by William E Clark and has been published by Walzone Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-08 with Computers categories.

Detectron2 Unlocked: A Hands-On Guide to Object Detection, Instance Segmentation, and Production Deployment offers a clear, expertly guided introduction to Detectron2, Facebook AI's modular computer vision library. The book grounds readers in essential concepts and architecture, explains supported tasks—object detection, instance segmentation, keypoint and panoptic segmentation—and situates Detectron2 alongside alternatives like MMDetection and the TensorFlow Object Detection API to help you choose the right tool for your needs. Building on that foundation, the guide walks through end-to-end workflows with pragmatic, example-driven chapters on dataset integration and annotation, advanced augmentations, configuration management, and custom model development. You will learn to reengineer data pipelines, extend trainers and plugins, run distributed and mixed-precision training, and optimize builds for diverse hardware and cloud environments—culminating in robust strategies for deploying performant models to production. The final section translates techniques into real-world impact with cross-industry case studies—from medical imaging and robotics to retail and smart cities—together with benchmarking methodologies and deployment patterns. Forward-looking chapters explore integrating vision transformers, self-supervised learning, and other emerging trends, while also addressing community collaboration and sustainability practices. This practical, example-rich volume equips researchers and practitioners to unlock Detectron2’s full potential in both research and production.

Advanced Technologies Systems And Applications Ix

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Author : Naida Ademović
language : en
Publisher: Springer Nature
Release Date : 2024-09-30

Advanced Technologies Systems And Applications Ix written by Naida Ademović and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-30 with Computers categories.

This book is a comprehensive compilation of articles that delve into the forefront of interdisciplinary applications of innovative technologies. It presents the scientific inquiries and outcomes showcased at the 15th Days of the Bosnian-Herzegovinian American Academy of Arts and Sciences conference, held in Sarajevo, Bosnia and Herzegovina, from June 20 to 23, 2024. The collection highlights the latest advancements and will draw the interest of researchers in diverse domains of engineering, including civil engineering, data science and geographic information systems, computer science and artificial intelligence, advanced environmental engineering and project management, information and communication technologies, and advanced electrical power systems. This book serves as a testament to the ongoing pursuit of knowledge and innovation in these fields, offering insights into the current research landscape and future directions. The contributions not only expand the theoretical foundations but also explore practical applications that address contemporary challenges in technology and engineering. The editors gratefully acknowledge the dedicated efforts of all the symposia chairs of the 15th Days of BHAAAS whose meticulous planning and scholarly oversight have enriched this book and contributed to its scholarly significance.

Innovations In Computer Vision Communication Systems And Computational Intelligence

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Author : Suman Lata Tripathi
language : en
Publisher: CRC Press
Release Date : 2025-12-23

Innovations In Computer Vision Communication Systems And Computational Intelligence written by Suman Lata Tripathi and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-23 with Computers categories.

The First International Conference on Computer Vision, Communication Systems, and Computational Intelligence (CVCNCE 2025), organised by the Department of Electronics and Communication Engineering at Francis Xavier Engineering College, Tirunelveli, was held on 08–09 May 2025 in hybrid mode. The conference brought together global researchers, academicians, and industry professionals to share pioneering ideas and advancements in computer vision, communication technologies, and computational intelligence. With keynote lectures, technical paper presentations, and panel discussions, CVCNCE 2025 served as a vibrant platform for knowledge exchange and interdisciplinary collaboration. The published proceedings highlight innovative research contributions that address emerging challenges and opportunities in modern technology.

Proceedings Of Third Emerging Trends And Technologies On Intelligent Systems

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Author : Arti Noor
language : en
Publisher: Springer Nature
Release Date : 2023-09-19

Proceedings Of Third Emerging Trends And Technologies On Intelligent Systems written by Arti Noor and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-19 with Technology & Engineering categories.

This book presents best selected papers presented at the International Conference on Emerging Trends and Technologies on Intelligent Systems (ETTIS 2023) held from 23 – 24 February 2023 in hybrid mode at C-DAC, Noida, India. The book includes current research works in the areas of artificial intelligence, big data, cyber-physical systems, and security in industrial/real-world settings. The book illustrates on-going research results, projects, surveying works, and industrial experiences that describe significant advances in all of the related areas.

Pixel Level Instance Segmentation Using Single Shot Detectors And Semantic Segmentation Networks

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Author : Akash
language : en
Publisher:
Release Date : 2019

Pixel Level Instance Segmentation Using Single Shot Detectors And Semantic Segmentation Networks written by Akash and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.

Object detection algorithms are vital in computer vision applications especially the one regarding segmentation of objects in a scene. Instance segmentation using mask RCNN is the state of the art computer vision application using deep neural networks. However it has a complex pipeline for training and real-time testing. Single shot detectors based on CNN such as YOLO are robust in real time object detection compared to Faster RCNN. Combining convolutional networks for semantic segmentation along with single shot object detectors can give superior instance segmentation in real time at less computational complexity. Independent training of instance detection and segmentation networks makes it possible to create application specific pipelines based on context. Lighter architecture using single shot detectors and segmentation networks reduces latency on common processors thereby achieving higher frame rate. Class specific training of detectors improves the average precision of detection. The entire architecture is developed in python over Keras deeplearning framework.OpenCV library is used to render and process the input and output images.

Deep Learning In Object Recognition Detection And Segmentation

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Author : Xiaogang Wang
language : en
Publisher: Foundations and Trends (R) in Signal Processing
Release Date : 2016-07-14

Deep Learning In Object Recognition Detection And Segmentation written by Xiaogang Wang and has been published by Foundations and Trends (R) in Signal Processing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-14 with categories.

Deep Learning in Object Recognition, Detection, and Segmentation provides a comprehensive introductory overview of a topic that is having major impact on many areas of research in signal processing, computer vision, and machine learning.

Simultaneous Object Detection And Segmentation Using Top Down And Bottom Up Processing

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Author : Vinay Sharma
language : en
Publisher:
Release Date : 2008

Simultaneous Object Detection And Segmentation Using Top Down And Bottom Up Processing written by Vinay Sharma and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Computer vision categories.

Abstract: This thesis addresses the fundamental tasks of detecting objects in images, recovering their location, and determining their silhouette shape. We focus on object detection techniques that 1) enable simultaneous recovery of object location and object shape, 2) require minimal manual supervision during training, and 3) are capable of consistent performance under varying imaging conditions found in real-world scenarios. The work described here results in the development of a unified method for simultaneously acquiring both the location and the silhouette shape of specific object categories in outdoor scenes. The proposed algorithm integrates top-down and bottom-up processing, and combines cues from these processes in a balanced manner. The framework provides the capability to incorporate both appearance and motion information, making use of low-level contour-based features, mid-level perceptual cues, and higher-level statistical analysis. A novel Markov random field formulation is presented that effectively integrate the various cues from the top-down and bottom-up processes. The algorithm attempts to leverage the natural structure of the world, thereby requiring minimal user supervision during training. Extensive experimental evaluation shows that the approach is applicable to different object categories, and is robust to challenging conditions such as large occlusions and drastic changes in viewpoint. For static camera scenarios, we present a contour-based background-subtraction technique. Utilizing both intensity and gradient information, the algorithm constructs a fuzzy representation of foreground boundaries called a Contour Saliency Map. Combined with a low-level data-driven approach for contour completion and closure, the approach is able to accurately recover object shape. We also present object detection and segmentation approaches that combine information from visible and thermal imagery. For object detection, we present a contour-based fusion algorithm for background-subtraction. We also introduce a feature-selection approach for object segmentation from multiple imaging modalities. Starting from an incomplete segmentation from one sensor, the approach automatically extracts relevant information from other sensors to generate a complete segmentation of the object. The algorithm utilizes criteria based on Mutual Information for defining feature relevance, and does not rely on a training phase.

Object Detection And Segmentation Based On Multiple Landmarks Using Tree Features And Regression

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Author :
language : de
Publisher:
Release Date : 2016

Object Detection And Segmentation Based On Multiple Landmarks Using Tree Features And Regression written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.