Machine Learning

Object Classification with CNNs using the Keras Deep Learning Library

Last Updated on August 27, 2020

Keras is a Python library for deep learning that wraps the powerful numerical libraries Theano and TensorFlow.

A difficult problem where traditional neural networks fall down is called object recognition. It is where a model is able to identify the objects in images.

In this post, you will discover how to develop and evaluate deep learning models for object recognition in Keras. After completing this tutorial you will know:

  • About the CIFAR-10 object classification dataset and how to load and use it in Keras.
  • How to create a simple Convolutional Neural Network for object recognition.
  • How to lift performance by creating deeper Convolutional Neural Networks.

Kick-start your project with my new book Deep Learning With Python, including step-by-step tutorials and the Python source code files for all examples.

Let’s get started.

  • Update Oct/2016: Updated for Keras 1.1.0 and TensorFlow 0.10.0.
  • Update Mar/2017: Updated for Keras 2.0.2, TensorFlow 1.0.1 and Theano 0.9.0.
  • Update Sep/2019: Updated or Keras 2.2.5 API.

For an extended tutorial on developing a CNN for CIFAR-10, see the post:

The CIFAR-10 Problem Description

The problem of automatically classifying photographs of objects is difficult because of the near infinite number
To finish reading, please visit source site