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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-cnn-opencv-covid19-face-mask-detection/
课程评论：没有评论
本次课程 “Data Science:Deep Learning-CNN & OpenCV -Face Mask Detection” 旨在教授学员如何利用人工智能和机器学习算法构建一个高精度的口罩佩戴检测模型。课程将涵盖从数据探索、数据增强、数据生成器到使用预训练模型（如 MobileNetV2）进行模型构建、评估以及在静态图像和实时视频流中进行口罩检测的全过程。 课程内容结构清晰，共分为多个任务： * **项目基础**：项目概述、Google Colab 介绍、项目和数据集的文件夹结构解析、数据加载。 * **数据处理与增强**：导入常用库、配置文件解读、数据增强技术实现、数据生成器实现。 * **深度学习模型构建**：卷积神经网络 (CNN) 和 OpenCV 基础、预训练模型概念、MobileNetV2 模型详解、构建和训练模型（包括优化器 Adam、损失函数 binary cross entropy、Epoch 和 Batch Size 的设置）、模型拟合与评估（分类报告、精度与损失可视化）、模型序列化。 * **口罩检测应用**：使用预训练的 Caffe 模型进行人脸检测、加载训练好的口罩检测模型、提取人脸特征、在图像中应用口罩检测模型、在视频流中实时检测口罩佩戴情况。 课程强调实践操作，学员将学习如何应用 CNN 和 OpenCV 技术，并获得 AutomationGig 的结业证书。所有课程数据集、Jupyter Notebook 和项目文件都将在课程资源部分提供。 这门课程特别适合希望掌握深度学习在实际应用中（如疫情期间的公共场所安全管理）的学员。
If you want to learn the process to detect whether a person is wearing a face mask using AI and Machine Learning algorithms then this course is for you.In this course I will cover, how to build a Face Mask Detection model to detect and predict whether a person is wearing a face mask or not in both static images and live video streams with very high accuracy using Deep Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model using CNN and OpenCV.This course will walk you through the initial data exploration and understanding, Data Augumentation, Data Generators, customizing pretrained Models like MobileNetV2, model building and evaluation. Then using the trained model to detect the presence of face mask in images and video streams.I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.Task 1 : Project Overview.Task 2 : Introduction to Google Colab.Task 3 : Understanding the project folder structure.Task 4 : Understanding the dataset and the folder structure.Task 5 : Loading the data from Google Drive.Task 6 : Importing the Libraries.Task 7 : About Config and Resize File.Task 8 : Some common Methods and UtilitiesTask 9 : About Data Augmentation.Task 10: Implementing Data Augmentation techniques.Task 11: About Data Generators.Task 12: Implementing Data Generators.Task 13: About Convolutional Neural Network (CNN).Task 14: About OpenCV.Task 15: Understanding pre-trained models.Task 16: About MobileNetV2 model.Task 17: Loading the MobileNetV2 classifier.Task 18: Building a new fully-connected (FC) head.Task 19: Building the final model.Task 20: Role of Optimizer in Deep Learning.Task 21: About Adam Optimizer.Task 22: About binary cross entropy loss function.Task 23: Putting all together.Task 24: About Epoch and Batch SizeTask 25: Model Fitting.Task 26: Predicting on the test data.Task 27: About Classification Report.Task 28: Classification Report in action.Task 29: Plot training and validation accuracy and loss.Task 30: Serialize/Writing the mode to disk.Task 31: About Pretrained Caffe models for Face Detection.Task 32: Loading the face detection model from drive.Task 33: Loading the mask detection model from drive.Task 34: Extracting the Face Detections.Task 35: Using the trained mask detection model to predict face mask on images.Task 36: Importing Libraries.Task 37: Function to detect and predict whether mask is present on a person's face in a video.Task 38: Loading our serialized face detector model from disk.Task 39: Loading the face mask detector model from disk.Task 40: Predicting face masks while looping over the video streams.We all know the impact that COVID19 has made in our daily life and how face masks are becoming a new normal in our day to day life. Face masks have become one of the most important tool to stop or reduce the spread of the virus. In this course we will see how we can build a model to classify whether a person is wearing a face mask or not and the same can be used in crowded areas like malls, bus stand, etc.Take the course now, and have a much stronger grasp of Deep learning in just a few hours!You will receive:1. Certificate of completion from AutomationGig.2. All the datasets used in the course are in the resources section.3. The Jupyter notebook and other project files are provided at the end of the course in the resource section.So what are you waiting for?Grab a cup of coffee, click on the ENROLL NOW Button and start learning the most demanded skill of the 21st century. We'll see you inside the course!Happy Learning!![Please note that this course and its related contents are for educational purpose only][Music: bensound]
