Object Localization with TensorFlow

4.4
50 個評分
提供方
Coursera Project Network
2,564 人已註冊
在此免費指導項目中,您將:

Create synthetic data for model training

Create and train a multi output neural network to perform object localization

Create custom metrics and calbacks in Keras

在面試中展現此實踐經驗

Clock2 hours
Intermediate中級
Cloud無需下載
Video分屏視頻
Comment Dots英語(English)
Laptop僅限桌面

Welcome to this 2 hour long guided project on creating and training an Object Localization model with TensorFlow. In this guided project, we are going to use TensorFlow's Keras API to create a convolutional neural network which will be trained to classify as well as localize emojis in images. Localization, in this context, means the position of the emojis in the images. This means that the network will have one input and two outputs. Think of this task as a simpler version of Object Detection. In Object Detection, we might have multiple objects in the input images, and an object detection model predicts the classes as well as bounding boxes for all of those objects. In Object Localization, we are working with the assumption that there is just one object in any given image, and our CNN model will classify and localize that object. Please note that you will need prior programming experience in Python. You will also need familiarity with TensorFlow. This is a practical, hands on guided project for learners who already have theoretical understanding of Neural Networks, Convolutional Neural Networks, and optimization algorithms like Gradient Descent but want to understand how to use use TensorFlow to solve computer vision tasks like Object Localization.

必備條件

Prior programming experience in Python. Conceptual understanding of Neural Networks. Prior experience with TensorFlow and Keras.

您要培養的技能

  • Deep Learning
  • Machine Learning
  • Tensorflow
  • Computer Vision
  • keras

分步進行學習

在與您的工作區一起在分屏中播放的視頻中,您的授課教師將指導您完成每個步驟:

  1. Introduction

  2. Download and Visualize Data

  3. Create Examples

  4. Plot Bouding Boxes

  5. Data Generator

  6. Model

  7. Custom Metric: IoU

  8. Compile the Model

  9. Custom Callback

  10. Model Training

指導項目工作原理

您的工作空間就是瀏覽器中的雲桌面,無需下載

在分屏視頻中,您的授課教師會為您提供分步指導

授課教師

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