Object Localization with TensorFlow

50 calificaciones
ofrecido por
Coursera Project Network
2564 ya inscrito
En este Proyecto guiado gratuito, tú:

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

Demuestra esta experiencia práctica en una entrevista

Clock2 hours
CloudNo se necesita descarga
VideoVideo de pantalla dividida
Comment DotsInglés (English)
LaptopSolo escritorio

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.


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

Habilidades que desarrollarás

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

Aprende paso a paso

En un video que se reproduce en una pantalla dividida con tu área de trabajo, tu instructor te guiará en cada paso:

  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

Cómo funcionan los proyectos guiados

Tu espacio de trabajo es un escritorio virtual directamente en tu navegador, no requiere descarga.

En un video de pantalla dividida, tu instructor te guía paso a paso




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