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所在平台: Udemy |
课程主页: https://www.udemy.com/course/object-detection-image-classification-with-pytorch-ssd/
课程评论：没有评论
课程名称：使用Pytorch和SSD进行目标检测与图像分类 课程概述：欢迎参加“使用Pytorch和SSD进行目标检测与图像分类”课程。本课程是一个综合性项目基础课程，您将学习如何构建目标检测系统、制造缺陷检测系统、垃圾分类系统和破损道路分割模型，使用Pytorch、Keras、卷积神经网络、U-Net、YOLOv和DETR ResNet。这门课程是Python与计算机视觉的完美结合，提供了实践编程技能和提升软件开发技术知识的理想机会。 在引言部分，您将了解目标检测和图像分类的基本原理，逐步认识每个系统的运作方式。接下来的部分，您将学习如何从Kaggle下载数据集，该平台提供各行业高质量的数据集。在项目开始之前，您将学习基本的计算机视觉知识，比如使用OpenCV激活相机和处理图像。 项目开始后，我们会首先使用Faster R-CNN、SSD、YOLOv和DETR ResNet等预训练模型构建目标检测系统，这些模型允许您在不需要使用自己的数据进行训练的情况下，检测和分类对象。之后，我们将使用Keras和卷积神经网络构建制造缺陷检测模型，根据图像输入判断产品是有缺陷的还是良好的。该系统可以通过相机或上传的图片自动检查产品，从而减少工厂中的人工质量控制。 接下来，我们将构建一个垃圾分类模型，使用Keras和CNN来区分有机和无机垃圾。该系统能够通过分析垃圾图像，自动识别材料，如塑料瓶、食物垃圾和纸张，从而实现废物的分类与处理。然后，我们将构建一个破损道路图像分割模型，使用U-Net架构，这种架构广泛应用于像素级图像分割任务。该系统可以帮助用户识别图像中道路损坏或坑洼的区域，辅助基础设施维护和智慧城市规划。 最后，在课程结束时，我们将进行测试，以确保模型的准确性和系统的预期表现。我们将使用各种输入进行测试，如图像、短视频和实时摄像头视频流，以确保功能的完整性。 通过本课程，您将学习到以下内容： - 目标检测和图像分类的基本原理 - 目标检测系统的工作流程，包括输入图像处理、特征提取、区域提案、边界框、类别预测和后处理 - 图像分类系统的工作流程，包括数据收集、标注、预处理、模型选择、训练、验证、微调和预测新图像 - 使用OpenCV激活相机 - 使用Pytorch和SSD构建目标检测系统 - 使用Pytorch和Faster R-CNN构建目标检测系统 - 使用YOLOv构建目标检测系统 - 使用DETR ResNet构建目标检测系统 - 使用Keras和卷积神经网络构建制造缺陷检测模型 - 使用OpenCV构建制造缺陷检测系统 - 使用Keras和卷积神经网络构建垃圾分类模型 - 使用OpenCV构建垃圾分类系统 - 使用U-Net构建破损道路图像分割模型 - 使用OpenCV构建破损道路检测系统 - 使用多种输入（如图像和视频）测试目标检测和图像分类系统 该课程将为您提供宝贵的技术知识，帮助您在制造、废物管理、农业、零售和运输等行业提升业务效率。
Welcome to Object Detection & Image Classification with Pytorch & SSD course. This is a comprehensive project based course where you will learn how to build object detection system, manufacturing defect detection system, waste classification system, and broken road segmentation model using Pytorch, Keras, convolutional neural network, U net, YOLOv, single shot detector, and DETR ResNet. This course is a perfect combination between Python and computer vision, making it an ideal opportunity for you to practice your programming skills while improving your technical knowledge in software development. In the introduction session, you will learn the basic fundamentals of object detection and image classification, such as getting to know how each system works step by step. In the next section, you will learn how to find and download datasets from Kaggle, it is a platform that offers a wide range of high quality datasets from various industries. Before starting the project, you will learn the basics of computer vision like activating cameras and processing images using OpenCV. Afterward, we will start the project, firstly, we are going to build object detection system using Faster R CNN, SSD, YOLOv and Detection Transformers ResNet, those are pre trained models that enable you to detect and classify objects without the need to train them using your own data. Following that, we are going to build a manufacturing defect detection model using Keras and Convolutional Neural Network to classify whether a product is defective or in good condition based on image input. This system will enable users to automatically inspect products using camera or uploaded images, reducing the need for manual quality control checks in factories. Then, after that, we are also going to build a waste classification model using Keras and CNN to distinguish between organic and non organic waste. This system will enable users to automate waste sorting for recycling or disposal purposes by analyzing waste images and accurately identifying materials such as plastic bottles, food waste, papers. In the next section, we are going to build a broken road image segmentation model using the U Net architecture, which is widely used for pixel wise image segmentation tasks. This system will enable users to identify damaged or pothole areas on roads from images, which can assist in infrastructure maintenance and smart city planning.Lastly, at the end of the course, we will conduct testing to make sure the model accuracy is high and the system performs as expected. We will test the system using various inputs such as images, short videos, and real time camera feeds to ensure the features are fully functioning.Well, before getting into the course, we need to ask this question to ourselves, why should we build object detection and image classification models? Well, here is my answer, these models help businesses to automate tasks that were once manual and repetitive, reducing dependency on constant human supervision and improving consistency. This technology is very valuable in industries like manufacturing, waste management, agriculture, retail and transportation. By implementing these systems, businesses can significantly reduce human error and increase processing speed. This will lead to greater efficiency and cost saving.Below are things that you can expect to learn from this course:Learn the basic fundamentals of object detection and image classificationLearn how object detection system works, starting from input image processing, feature extraction, region proposal, bounding box, class prediction, and post processingLearn how image classification system works starting from data collection, labelling, preprocessing, model selection, training, validation, finetuning, and predicting new imageLearn how to activate camera using OpenCVLearn how to build object detection system using Pytorch and SSDLearn how to build object detection system using Pytorch and Faster R-CNNLearn how to build object detection system using YOLOvLearn how to build object detection system using DETR ResNetLearn how to build manufacturing defect detection model using Keras and Convolutional Neural NetworkLearn how to build manufacturing defect detection system using OpenCVLearn how to build waste classification model using Keras and Convolutional Neural NetworkLearn how to build waste classification system using OpenCVLearn how to build broken road image segmentation model using UnetLearn how to build broken road detection system using OpenCVLearn how to test object detection and image classification systems using variety of inputs like images and videos
