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所在平台: Udemy |
课程主页: https://www.udemy.com/course/training-yolo-v3-for-objects-detection-with-custom-data/
课程评论：没有评论
**课程名称：** 使用自定义数据训练 YOLO 进行对象检测 **课程概述：** 本课程是一门实践性很强的课程，将教授您如何使用 YOLO v3-v4 算法训练自定义对象检测器。课程从使用在 COCO 数据集上预训练的 YOLO v3-v4 模型开始，并通过 OpenCV 深度学习库实现对图像、视频和实时对象进行检测。您将获得可用于未来项目的代码模板，并能够将它们集成到您自己训练的 YOLO 检测器中。 随后，课程将引导您完成数据集的标注，并从中提取所需图像以创建自定义数据集。您还将学习如何将交通标志数据集转换为 YOLO 格式，所提供的代码模板可以进行修改以适应其他数据集。 在准备好数据集后，您将在 Darknet 框架中训练和测试 YOLO v3-v4 检测器。 作为奖励内容，您将学习如何使用 PyQt 构建一个用于 YOLO 对象检测的图形用户界面 (GUI)。这个项目不仅可以作为您向导师展示成果、向同学做演示的一个亮点，还可以为您的简历增添一笔有价值的经历。 **课程内容组织：** 本课程的每个部分都包含以下内容： * **视频讲座：** 具有 SMART（具体、可衡量、可实现、结果导向、有时限）目标的视频讲座，确保学习的有效性。 * **编码活动：** 通过动手实践来巩固所学知识。 * **代码模板：** 可供集成到您未来项目的代码示例。 * **测验：** 检验对课程内容的理解程度。 * **可下载说明：** 提供详细的步骤和指导。 * **讨论机会：** 与同学和讲师互动交流。
In this hands-on course, you'll train your own Object Detector using YOLO v3-v4 algorithms.As for beginning, you'll implement already trained YOLO v3-v4 on COCO dataset. You'll detect objects on image, video and in real time by OpenCV deep learning library. The code templates you can integrate later in your own future projects and use them for your own trained YOLO detectors.After that, you'll label individual dataset as well as create custom one by extracting needed images from huge existing dataset.Next, you'll convert Traffic Signs dataset into YOLO format. Code templates for converting you can modify and apply for other datasets in your future work.When datasets are ready, you'll train and test YOLO v3-v4 detectors in Darknet framework.As for Bonus part, you'll build graphical user interface for Object Detection by YOLO and by the help of PyQt. This project you can represent as your results to your supervisor or to make a presentation in front of classmates or even mention it in your resume.Content Organization. Each Section of the course contains:Video LecturesCoding ActivitiesCode TemplatesQuizzesDownloadable InstructionsDiscussion OpportunitiesVideo Lectures of the course have SMART objectives:S - specific (the lecture has specific objectives)M - measurable (results are reasonable and can be quantified)A - attainable (the lecture has clear steps to achieve the objectives)R - result-oriented (results can be obtained by the end of the lecture)T - time-oriented (results can be obtained within the visible time frame)
