Object Localization with TensorFlow

4.4
50件の評価
提供:
Coursera Project Network
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この無料ガイド付きプロジェクトでは、次のことを行います。

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

この実践的な経験を面接でアピールする

Clock2 hours
Intermediate中級
Cloudダウンロード不要
Video分割画面ビデオ
Comment Dots英語
Laptopデスクトップのみ

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.

必要事項

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

あなたが開発するスキル

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

ステップバイステップで学習します

ワークエリアを使用した分割画面で再生するビデオでは、講師がこれらの手順を説明します。

  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

ガイド付きプロジェクトの仕組み

ワークスペースは、ブラウザに完全にロードされたクラウドデスクトップですので、ダウンロードは不要です

分割画面のビデオで、講師が手順ごとにガイドします

レビュー

OBJECT LOCALIZATION WITH TENSORFLOW からの人気レビュー

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