基于Python和PyTorch的物体检测深度学习视频教程

基于PyTorch和Python深度学习的计算机视觉目标检测。训练和部署模型(探测器2、RCNN)，你准备好进入使用深度学习的物体检测的迷人世界了吗？在我们的综合课程“使用Python和PyTorch进行对象检测的深度学习”中，我们将指导您了解检测、分类和定位图像中的对象所需的基本概念和技术。目标检测在许多领域具有广泛的潜在现实应用。目标检测用于自主车辆感知和理解它们的周围环境。它有助于检测和跟踪行人、车辆、交通标志、交通灯和道路上的其他物体。对象检测用于监视和安全，使用无人机来识别和跟踪可疑活动、入侵者和感兴趣的对象。对象检测用于交通监控、头盔和车牌检测、运动员跟踪、缺陷检测、工业应用等等。借助Python编程和PyTorch深度学习框架的强大组合，您将探索最先进的算法和架构，如R-CNN、Fast RCNN和Fast RCNN。在整个课程中，您将对卷积神经网络(CNN)及其在对象检测中的作用有一个坚实的理解。您将学习如何利用预训练的模型，使用脸书人工智能研究所(FAIR)开发的Detectron2库对它们进行微调以进行对象检测。Deep Learning for Object Detection with Python and PyTorch

本课程涵盖了使用Python和PyTorch的深度学习进行对象检测的完整流程和实践经验，如下所示:使用Python和Pytorch协同学习对象检测使用深度学习模型学习对象检测卷积神经网络(CNN)简介学习RCNN、快速RCNN、 更快的RCNN和掩码RCNN架构使用更快的RCNN和RCNN执行对象检测脸书人工智能研究所(FAIR)的Detectron2简介使用Detectron2模型执行对象检测开发带注释的自定义对象检测数据集使用深度学习在自定义数据集上执行对象检测训练、测试、评估您自己的对象检测模型并可视化结果本课程结束时，您将具备开始将深度学习应用于您自己工作或研究中的对象检测问题所需的知识和技能。 无论你是计算机视觉工程师、数据科学家还是开发人员，这门课程都是让你对深度学习的理解更上一层楼的完美方式。让我们开始使用Python和PyTorch进行对象检测的深度学习这一激动人心的旅程。

由Mazhar Hussain创作
MP4 |视频:h264，1280×720 |音频:AAC，44.1 KHz，2声道
类型:电子学习|语言:英语|时长:16节课(1小时46分钟)|大小:801 MB

你会学到什么
使用Python和Pytorch编码学习对象检测
使用深度学习模型学习对象检测
卷积神经网络(CNN)简介
了解RCNN、快速RCNN、更快RCNN和屏蔽RCNN架构
使用快速RCNN和更快的RCNN执行对象检测
脸书人工智能研究所介绍探测器2
用探测器2模型执行物体探测
探索带注释的自定义对象检测数据集
使用深度学习对自定义数据集执行对象检测
训练、测试、评估您自己的对象检测模型并可视化结果

要求
本课程将按照从零到英雄的完整流程，通过Python和PyTorch使用深度学习进行对象检测
没有语义分割的先验知识。所有内容都将包含实际操作培训
要开始使用Google Colab编写Python代码，需要一个Google Gmail帐户

这门课程是给谁的
本课程面向广泛的学生和专业人士，包括但不限于:机器学习工程师、深度学习工程师、数据科学家、计算机视觉工程师以及希望学习如何使用PyTorch构建和训练用于对象检测的深度学习模型的研究人员
总的来说，该课程面向任何希望学习如何使用深度学习从视觉数据中提取意义，并更深入地了解使用Python和PyTorch进行对象检测的理论和实际应用的人

Object Detection for Computer Vision using Deep Learning with PyTorch & Python. Train & Deploy Models (Detectron2, RCNN)

What you’ll learn
Learn Object Detection with Python and Pytorch Coding
Learn Object Detection using Deep Learning Models
Introduction to Convolutional Neural Networks (CNN)
Learn RCNN, Fast RCNN, Faster RCNN and Mask RCNN Architectures
Perform Object Detection with Fast RCNN and Faster RCNN
Introduction to Detectron2 by Facebook AI Research (FAIR)
Preform Object Detection with Detectron2 Models
Explore Custom Object Detection Dataset with Annotations
Perform Object Detection on Custom Dataset using Deep Learning
Train, Test, Evaluate Your Own Object Detection Models and Visualize Results

Requirements
Object Detection using Deep Learning with Python and PyTorch is taught in this course by following a complete pipeline from Zero to Hero
No prior knowledge of Semantic Segmentation is assumed. Everything will be covered with hands-on trainings
A Google Gmail account is required to get started with Google Colab to write Python Code

Description
Are you ready to dive into the fascinating world of object detection using deep learning? In our comprehensive course “Deep Learning for Object Detection with Python and PyTorch”, we will guide you through the essential concepts and techniques required to detect, classify, and locate objects in images. Object Detection has wide range of potential real life application in many fields. Object detection is used for autonomous vehicles to perceive and understand their surroundings. It helps in detecting and tracking pedestrians, vehicles, traffic signs, traffic lights, and other objects on the road. Object Detection is used for surveillance and security using drones to identify and track suspicious activities, intruders, and objects of interest. Object Detection is used for traffic monitoring, helmet and license plate detection, player tracking, defect detection, industrial usage and much more.With the powerful combination of Python programming and the PyTorch deep learning framework, you’ll explore state-of-the-art algorithms and architectures like R-CNN, Fast RCNN and Faster R-CNN. Throughout the course, you’ll gain a solid understanding of Convolutional Neural Networks (CNNs) and their role in Object Detection. You’ll learn how to leverage pre-trained models, fine-tune them for Object Detection using Detectron2 Library developed by by Facebook AI Research (FAIR).The course covers the complete pipeline with hands-on experience of Object Detection using Deep Learning with Python and PyTorch as follows:Learn Object Detection with Python and Pytorch CodingLearn Object Detection using Deep Learning ModelsIntroduction to Convolutional Neural Networks (CNN)Learn RCNN, Fast RCNN, Faster RCNN and Mask RCNN ArchitecturesPerform Object Detection with Fast RCNN and Faster RCNNIntroduction to Detectron2 by Facebook AI Research (FAIR)Preform Object Detection with Detectron2 ModelsExplore Custom Object Detection Dataset with AnnotationsPerform Object Detection on Custom Dataset using Deep LearningTrain, Test, Evaluate Your Own Object Detection Models and Visualize ResultsBy the end of this course, you’ll have the knowledge and skills you need to start applying Deep Learning to Object Detection problems in your own work or research. Whether you’re a Computer Vision Engineer, Data Scientist, or Developer, this course is the perfect way to take your understanding of Deep Learning to the next level. Let’s get started on this exciting journey of Deep Learning for Object Detection with Python and PyTorch.

Who this course is for
This course is designed for a wide range of Students and Professionals, including but not limited to: Machine Learning Engineers, Deep Learning Engineers, Data Scientists, Computer Vision Engineers, and Researchers who want to learn how to use PyTorch to build and train deep learning models for Object Detection
In general, the course is for anyone who wants to learn how to use Deep Learning to extract meaning from visual data and gain a deeper understanding of the theory and practical applications of Object Detection using Python and PyTorch