Object Localization with TensorFlow

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在此免费指导项目中,您将:

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

在面试中展现此实践经验

2 hours
中级
无需下载
分屏视频
英语(English)
仅限桌面

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.

必备条件

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

您要培养的技能

  • Deep Learning

  • Machine Learning

  • Tensorflow

  • Computer Vision

  • keras

分步进行学习

在与您的工作区一起在分屏中播放的视频中,您的授课教师将指导您完成每个步骤:

  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

指导项目工作原理

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在分屏视频中,您的授课教师会为您提供分步指导

授课教师

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