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所在平台: Udemy |
课程主页: https://www.udemy.com/course/yolo-custom-object-detection/
课程评论：没有评论
**课程名称：** YOLO：Python自定义对象检测与Web应用 **课程概述：** 本课程专注于计算机视觉领域中应用最广泛的技术之一——目标检测。我们将深入学习强大的统一目标检测模型YOLO（You Only Look Once），它利用神经网络实现实时目标检测，并以其速度和准确性而著称，广泛应用于交通信号、行人、停车计时器和动物等检测场景。 课程分为两大部分： 1. **自定义数据集目标检测：** * **理论基础：** 讲解目标检测的历史、YOLO的数学原理、不同版本YOLO的演进，以及IoU、精确率、mAP等关键目标检测指标。 * **环境准备：** 指导学员安装Python及相关库，确保开发环境配置完善。 * **数据准备：** * 数据收集的指导原则。 * 使用开源工具LabelImg进行图像标注。 * 解析XML文件，提取文件名、图像尺寸、边界框信息（xmin, xmax, ymin, ymax）。 * 利用Pandas数据框处理数据，并将数据集划分为训练集和测试集。 2. **YOLOv5模型训练与Web应用开发：** * 使用YOLOv5模型进行实际训练。 * 使用Python开发一个Web应用，为用户提供图形用户界面（GUI）体验。 * 将训练好的模型部署到云平台。 **课程资源：** 所有使用的代码、图像和模型权重均已提供下载链接。学员可自由将其用于个人项目。 **课程成果：** 完成课程后，学员将获得课程结业证书，为个人作品集增添价值。
Welcome to 'YOLO: Custom Object Detection & Web App in Python'Object Detection is the most used applications of Computer Vision, where computer/machine can able to locate and classify the object in an image.In this course we specifically using YOLO (You Only Look Once) and powerful and popular unified object detection model. YOLO uses neural networks to provide real-time object detection. This algorithm is popular because of its speed and accuracy. It has been used in various applications to detect traffic signals, people, parking meters, and animals.This course is divided into two halves. The first half deals with object detection with custom dataset where we will locate 20 classes of objects. And in second half we will create an web app and give the Graphical User Interphase experience to the use. Not only that we will also deploy our model in Cloud platform.Now let us see the topics in the courseIntroductory theory session about YOLO Object DetectionHere in this section I will explain history of Object DetectionObject Detection Metrics like IoU (Intersection Over Union), Precision, mean Average Precision (mAP) etc.Then we will see the mathematical concept behind YOLOAlso I will cover how YOLO improved from each versionAfter that, we are ready to proceed with preparing our computer for Python coding by downloading and installing the Python package and will check and see if everything is installed fine. 2. Data Preparation for YOLO model In this section we will put every we learn in to practice. This section is completely hands-on where we will do python code and use pandas dataframes to prepare the data. a. Thumb rules to follow in Collect Data b. Label image for object detection: Here we will use LabelImg tool which is an open source tool to label the label. c. Parse data from XML files and extract information like filename, size, bounding box info like (xmin, xmax, ymin, ymax) d. Process the data from XML in pandas dataframe. And then split the image and save the respective label information information in train and test.3. Train YOLO v5 Model4. Develop Web App in PythonThat's all about the topics which are currently included in this quick course. The code, images and weights used in this course has been uploaded and shared in a folder. I will include the link to download them in the last session or the resource section of this course. You are free to use the code in your projects with no questions asked.Also after completing this course, you will be provided with a course completion certificate which will add value to your portfolio.
