|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/advanced-computer-vision-with-tensorflow
课程评论：没有评论
课程名称：使用TensorFlow的高级计算机视觉 课程概述： 本课程将带您深入探索计算机视觉领域，包括图像分类、图像分割、物体定位和物体检测等内容。学员将学习如何将迁移学习应用于物体定位和检测，并学习使用区域卷积神经网络（R-CNN）和ResNet-50等物体检测模型，定制现有模型，甚至构建自己的模型来检测、定位和标记橡皮鸭图像。此外，课程还将涵盖使用全卷积网络（FCN）变体（如U-Net和Mask R-CNN）进行图像分割，从而能够识别和检测数字、宠物、僵尸等对象。 学员还将学习如何利用类激活图和显著性图来识别模型用于预测的图像部分，并掌握这些机器学习解释方法，以检查和改进著名网络AlexNet的设计。 DeepLearning.AI TensorFlow：高级技术专业化将介绍TensorFlow的特性，为学习者提供更多控制模型架构的能力以及帮助构建和训练高级机器学习模型的工具。本专业化适合有基础TensorFlow知识的早期和中期的软件及机器学习工程师，旨在拓展他们的知识和技能，以利用高级TensorFlow特性构建强大的模型。 课程大纲： 1. 计算机视觉简介 - 概念概述，包括图像分类、物体定位、物体检测和图像分割，掌握多标签分类的描述，区分语义分割与实例分割。接下来的课程中学员将应用TensorFlow构建物体检测和图像分割模型。 2. 物体检测 - 了解流行物体检测模型概述，例如区域卷积神经网络和ResNet-50，使用TensorFlow Hub检索物体检测模型，下载自己的模型并配置训练，还将构建自己的物体检测模型，通过迁移学习训练有效地定位橡皮鸭。 3. 图像分割 - 本周重点学习利用全卷积神经网络进行图像分割，能够为每个像素分配类别标签，相比边界框能进行更加详细的对象识别。本周学员将构建全卷积神经网络、U-Net和Mask R-CNN，以识别和检测数字、宠物和僵尸等对象。 4. 可视化与可解释性 - 了解模型可解释性的重要性，并实现类激活图、显著性图及加权类激活图，以识别模型用于做出预测的图像部分。此外，将通过可视化模型中间层的激活，来改进著名网络AlexNet的设计。
Name:Introduction to Computer Vision
Description:Get a conceptual overview of image classification, object localization, object detection, and image segmentation. Also be able to describe multi-label classification, and distinguish between semantic segmentation and instance segmentation. In the rest of this course, you will apply TensorFlow to build object detection and image segmentation models.
Name:Object Detection
Description:This week, you’ll get an overview of some popular object detection models, such as regional-CNN and ResNet-50. You’ll use object detection models that you’ll retrieve from TensorFlow Hub, download your own models and configure them for training, and also build your own models for object detection. By using transfer learning, you will train a model to detect and localize rubber duckies using just five training examples. You’ll also get to manually label your own rubber ducky images!
Name:Image Segmentation
Description:This week is all about image segmentation using variations of the fully convolutional neural network. With these networks, you can assign class labels to each pixel, and perform much more detailed identification of objects compared to bounding boxes. You’ll build the fully convolutional neural network, U-Net, and Mask R-CNN this week to identify and detect numbers, pets, and even zombies!
Name:Visualization and Interpretability
Description:This week, you’ll learn about the importance of model interpretability, which is the understanding of how your model arrives at its decisions. You’ll also implement class activation maps, saliency maps, and gradient-weighted class activation maps to identify which parts of an image are being used by your model to make its predictions. You’ll also see an example of how visualizing a model’s intermediate layer activations can help to improve the design of a famous network, AlexNet.
In this course, you will: a) Explore image classification, image segmentation, object localization, and object detection. Apply transfer learning to object localization and detection. b) Apply object detection models such as regional-CNN and ResNet-50, customize existing models, and build your own models to detect, localize, and label your own rubber duck images. c) Implement image segmentation using variations of the fully convolutional network (FCN) including U-Net and d) Mask-RCNN to identify and detect numbers, pets, zombies, and more. d) Identify which parts of an image are being used by your model to make its predictions using class activation maps and saliency maps and apply these ML interpretation methods to inspect and improve the design of a famous network, AlexNet. The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture and tools that help them create and train advanced ML models. This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.
