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所在平台: Udemy |
课程主页: https://www.udemy.com/course/yolo-video-object-detection-with-python/
课程评论：没有评论
本课程《YOLOv8：使用 Python 在自定义数据集上进行视频对象检测》旨在教授学员如何利用 YOLOv8 这一前沿技术进行高效、精准的视频对象检测。YOLOv8，即“You Only Look Once”，是一种先进的深度卷积神经网络，以其在视频中识别对象的惊人速度和准确性而闻名。 课程将全面介绍 YOLO 系列的所有变体，并重点教授如何使用最新的 YOLOv8 版本进行**实时视频对象检测**。YOLOv8 相较于之前的版本，在速度和准确性上均有显著提升。其核心优势在于能够**一次性处理整个图像**，预测对象的边界框及其类别，从而实现计算上的高效。YOLOv8 提供五种不同参数量的变体：nano (n)、small (s)、medium (m)、large (l) 和 extra large (x)，学员可根据自身需求选择合适的模型。 YOLOv8 不仅限于对象检测，它是一个支持多种计算机视觉任务的 AI 框架，还可以用于**图像分割、分类和姿态估计**。YOLOv8 的速度和检测精度使其成为**实时应用**（如视频监控和自动驾驶）的理想选择。课程将探讨其在公共空间安全监控、**体育分析**（如足球比赛中的球员和动作检测）以及**零售分析**（如库存管理和顾客行为追踪）等实际场景中的应用。 课程将系统地讲解对象检测的概念，即在图像或视频流中识别对象的**位置和类别**，输出包含边界框、类别标签和置信度得分的结果。 本课程提供完整的**端到端实践体验**，使用 Python 和 PyTorch 实现 YOLOv8 深度学习架构进行对象检测。 **课程重点内容包括：** * 使用 Python 进行 YOLOv8 **实时视频对象检测**。 * 在**自定义数据集**上训练、测试 YOLOv8 模型，并将其部署到实际项目中。 * YOLO 及其基于深度卷积神经网络的架构介绍。 * YOLO 对象检测的工作原理。 * CNN、RCNN、Fast RCNN 和 Faster RCNN 的概述。 * YOLO 系列（YOLOv2 至 YOLOv7）的概览。 * YOLOv8 及其架构详解。 * **自定义足球运动员数据集**的配置。 * 在 Google Colab 中进行 Python 代码编写的环境设置。 * YOLOv8 Ultralytics 及其**超参数设置**。 * 使用 YOLOv8 训练**球员、裁判和足球检测**模型。 * 在视频和图像上**测试训练好的 YOLOv8 模型**。 * **导出 YOLOv8 模型**为所需格式进行部署。 通过本课程，学员将获得实践经验，能够将 YOLOv8 的能力应用于**特定用例**。掌握使用 Python 和 YOLOv8 进行视频对象检测，将使学员能够为各个领域的创新做出贡献，重塑计算机视觉应用的未来。课程将提供完整的 Python 代码和数据集，帮助学员快速上手。
Unlock the potential of YOLOv8, a cutting-edge technology that revolutionizes video Object Detection. YOLOv8, or "You Only Look Once," is a state-of-the-art Deep Convolutional Neural Network renowned for its speed and accuracy in identifying objects within videos. In our course, "YOLOv8: Video Object Detection with Python on Custom Dataset" you'll explore its applications across various real-world scenarios. In this course, You will have the overview of all YOLO variants Where you will perform the real time video object detection with latest YOLO version 8 which is extremely fast and accurate as compared to the previous YOLO versions. YOLOv8 processes an entire image in a single pass to predict object bounding box and its class, making object detection computationally efficient. YOLOv8 comes in five variants based on the number of parameters - nano(n), small(s), medium(m), large(l), and extra large(x). You can use all the variants for object detection according to your requirement.YOLOv8 is an AI framework that supports multiple computer vision tasks. YOLO8 can be used to perform Object Detection, Image segmentation, classification, and pose estimation. Speed and Detection accuracy of YOLOv8 makes it so popular for real-time applications such as object detection in videos and surveillance as compared to other object detectors. Imagine deploying YOLOv8 to monitor crowded public spaces for security, effortlessly tracking objects in surveillance videos, or enhancing autonomous vehicles' perception capabilities. Witness its capabilities in sports analytics, precisely detecting players and actions in dynamic game scenarios like football matches. Dive into retail analytics, where YOLOv8 can optimize inventory management and customer experience by tracking products and people movements.Object detection is a task that involves identifying the location and class of objects in an image or video stream. The output of an object detector is a set of bounding boxes that enclose the objects in the image, along with class labels and confidence scores for each box. Object detection is a good choice when you need to identify objects of interest in a scene. This course covers the complete pipeline with hands-on experience of Object Detection using YOLOv8 Deep Learning architecture with Python and PyTorch as follows: Course Breakdown: Key Learning OutcomesYOLOv8 for Real-Time Video Object Detection with PythonTrain, Test YOLO8 on Custom Dataset and Deploy to Your Own ProjectsIntroduction to YOLO and its Deep Convolutional Neural Network based Architecture. How YOLO Works for Object Detection?Overview of CNN, RCNN, Fast RCNN, and Faster RCNNOverview of YOLO Family (YOLOv2, YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7 )What is YOLOv8 and its Architecture?Custom Football Player Dataset Configuration for Object DetectionSetting-up Google Colab for Writing Python codeYOLOv8 Ultralytics and its HyperParameters SettingsTraining YOLOv8 for Player, Referee and Football DetectionTesting YOLOv8 Trained Models on Videos and ImagesDeploy YOLOv8: Export Model to required FormatThis course provides you with hands-on experience, enabling you to apply YOLOv8's capabilities to your specific use cases. By mastering video object detection with Python and YOLOv8, you'll be equipped to contribute to innovations in diverse fields, reshaping the future of computer vision applications. Join us and discover the limitless possibilities of YOLOv8 in the real world! I will provide you the complete python code and datasets for real time video Object Detection with Python, so that you can start within no time. Let's enroll now and get started. See you inside the class.
