Structure of the CIFAR10 dataset
- (x_train, x_test): These variables contain the pixel data for the images.
- x_train is the training set of the images, and
- x_test is the testing set.
- The images are 32×32 pixels in size and are represented as a numpy array of shape (32, 32, 3), where 3 stands for the three color channels (RGB).
- (y_train, y_test): These are the corresponding labels for the images. Each label is an integer from 0 to 9, representing the class of representation, i.e.:
- (Label) -> (Class)
- 0 -> Airplane
- 1 -> Automobile
- 2 -> Bird
- 3 -> Cat
- 4 -> Deer
- 5 -> Dog
- 6 -> Frog
- 7 -> Horse
- 8 -> Ship
- 9 -> Truck
CIFAR10 DataSet in Keras (Tensorflow) for Object Recognition
The CIFAR-10 dataset is readily accessible in Python through the Keras library, which is part of TensorFlow, making it a convenient choice for developers and researchers working on machine learning projects, especially in image classification. In this article, we will explore CIFAR10 (classification of 10 image labels) from Keras/tensorflow.
Table of Content
- What is the CIFAR10 Keras/Tensorflow Datasets?
- Characteristics of CIFAR10 Dataset
- How to Load CIFAR10 (classification of 10 image labels) keras Datasets?
- Significance of CIFAR10 in Machine Learning
- Applications of the CIFAR10 Dataset:
- FAQ – CIFAR10 – Keras/Tensorflow Datasets
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