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所在平台: Udemy |
课程主页: https://www.udemy.com/course/object-detection-with-python/
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课程名称：使用Python和PyTorch进行深度学习目标检测 课程概述： 准备好深入探索使用深度学习进行目标检测的迷人世界了吗？在我们的综合课程“使用Python和PyTorch进行深度学习目标检测”中，我们将指导您了解检测、分类和定位图像中的物体所需的基本概念和技术。目标检测在许多领域具有广泛的实际应用。它被用于自动驾驶汽车，以感知和理解周围环境，帮助检测和跟踪行人、车辆、交通标志、红绿灯以及路上的其他物体。此外，目标检测也被用于监控和安全，通过无人机识别和跟踪可疑活动、入侵者及其他重要物体。 通过结合Python编程和PyTorch深度学习框架，您将探索状态-of-the-art的算法和架构，如R-CNN、Fast RCNN和Faster R-CNN。在整个课程中，您将对卷积神经网络（CNN）及其在目标检测中的作用有一个扎实的理解。您将学习如何利用预训练模型，并使用Facebook人工智能研究院（FAIR）开发的Detectron2库进行微调，以实现目标检测。 课程内容包括： - 使用Python和PyTorch的目标检测编码 - 使用深度学习模型进行目标检测 - 卷积神经网络（CNN）简介 - 学习RCNN、Fast RCNN、Faster RCNN、Mask RCNN和YOLOv8架构 - 使用Fast RCNN和Faster RCNN进行目标检测 - 使用YOLOv8进行实时视频目标检测 - 训练、测试和部署YOLOv8进行视频目标检测 - Facebook AI Research (FAIR)的Detectron2介绍 - 使用Detectron2模型进行目标检测 - 探索带注释的自定义目标检测数据集 - 在自定义数据集上执行深度学习目标检测 - 训练、测试、评估自己的目标检测模型并可视化结果 - 使用Mask RCNN执行像素级目标实例分割 - 在自定义数据集上使用PyTorch和Python进行目标实例分割 通过本课程的学习，您将掌握将深度学习应用于目标检测问题所需的知识和技能，无论您是计算机视觉工程师、数据科学家还是开发人员，此课程都是提升您对深度学习理解的完美途径。让我们开始这段激动人心的深度学习目标检测旅程吧！
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, Mask RCNN and YOLO8 ArchitecturesPerform Object Detection with Fast RCNN and Faster RCNNPerform Real-time Video Object Detection with YOLOv8Train, Test and Deploy YOLOv8 for Video Object DetectionIntroduction 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 ResultsPerform Object Instance Segmentation at Pixel Level using Mask RCNNPerform Object Instance Segmentation on Custom Dataset with Pytorch and PythonBy 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.
