Object Localization with TensorFlow

4.3

66 個評分

提供方

4,055 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

2 hours
中級
無需下載
分屏視頻
英語(English)
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

審閱

來自OBJECT LOCALIZATION WITH TENSORFLOW的熱門評論

查看所有評論

常見問題

由於您的工作空間包含適合筆記本電腦或台式計算機使用的雲桌面,因此指導項目不在移動設備上提供。

指導項目授課教師是特定領域的專家,他們在項目的技能、工具或領域方面經驗豐富,並且熱衷於分享自己的知識以影響全球數百萬的學生。

您可以從指導項目中下載並保留您創建的任何文件。為此,您可以在訪問云桌面時使用‘文件瀏覽器’功能。

您可在頁面頂部點按此指導項目的經驗級別,查看任何知識先決條件。對於指導項目的每個級別,您的授課教師會逐步為您提供指導。

是,您可以在瀏覽器的雲桌面中獲得完成指導項目所需的一切。

您可以直接在瀏覽器中於分屏環境下完成任務,以此從做中學。在屏幕的左側,您將在工作空間中完成任務。在屏幕的右側,您將看到有授課教師逐步指導您完成項目。