CIFAR 10 Dataset

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Visualization of the CIFAR 10 Train Dataset in the Deep Lake UI

CIFAR 10 Dataset

What is CIFAR 10 Dataset?

The CIFAR10 (Canadian Institute For Advanced Research) dataset consists of 10 classes with 6000 color images of 32×32 resolution for each class. It is divided into 50000 training and 10000 testing images. The test dataset contains exactly 1000 randomly collected images from each class. The training datasets may contain more images per class compared to others but they contain 5000 images per class on average.

Download CIFAR 10 Dataset in Python

Instead of downloading the CIFAR-10 dataset in Python, you can effortlessly load it in Python via our Deep Lake open-source with just one line of code.

Load CIFAR 10 Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/cifar10-train")

Load CIFAR 10 Dataset Testing Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/cifar10-test")

CIFAR 10 Dataset Structure

CIFAR 10 Data Fields

images: tensor containing images of the dataset
labels: tensor containing labels for their respective image

CIFAR 10 Data Splits

The CIFAR10 dataset training set is composed of 50,000 images and 10 classes.
The CIFAR10 dataset testing set is composed of 10,000 images and 10 classes.

How to use CIFAR 10 Dataset with PyTorch and TensorFlow in Python

Train a model on CIFAR 10 dataset with PyTorch in Python

Let’s use Deep Lake built-in PyTorch one-line dataloader to connect the data to the compute:

				
					dataloader = ds.pytorch(num_workers=0, batch_size=4, shuffle=False)

Train a model on CIFAR 10 dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Additional Information about CIFAR 10 Dataset

CIFAR 10 Dataset Description

Homepage: https://www.cs.toronto.edu/~kriz/cifar.html
Paper: https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf

CIFAR 10 Dataset Curators

Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton

CIFAR 10 Dataset Licensing Information

Deep Lake users may have access to a variety of publicly available datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have a license to use the datasets. It is your responsibility to determine whether you have permission to use the datasets under their license.

If you’re a dataset owner and do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thank you for your contribution to the ML community!

CIFAR 10 Dataset Citation Information

				
					@article{krizhevsky2009learning,
  title={Learning multiple layers of features from tiny images},
  author={Krizhevsky, Alex and Hinton, Geoffrey and others},
  year={2009},
  publisher={Citeseer}
}

CIFAR 10 Dataset FAQs

What is the CIFAR 10 dataset for Python?

The CIFAR 10 dataset contains images that are commonly used to train machine learning and computer vision algorithms. CIFAR 10 consists of 60000 32×32 images. These images are split into 10 mutually exclusive classes, with 6000 images per class. The classes are airplanes, automobiles, birds, cats, deer, dogs, frogs, horses, ships, and trucks.

How is CIFAR 10 data organized?

There are 50000 training images and 10000 test images in the CIFAR 10 dataset. The dataset contains five training batches and one test batch. The training batches contain exactly 5000 images from each class. The test batch contains exactly 1000 randomly-selected images from each class.

What is the CIFAR 10 dataset used for?

Alongside the MNIST dataset, CIFAR 10 is one of the most popular datasets in the field of machine learning research. It is an established computer vision dataset used for object recognition. CIFAR 10 is commonly used as a benchmark dataset for computer vision algorithms.

What is the difference between CIFAR 10 dataset and CIFAR 100 dataset?

The main difference between the CIFAR 10 dataset and the CIFAR 100 dataset is the number of images and classes. The CIFAR 10 dataset has 10 classes with 6000 images per class. The CIFAR 100 dataset has 100 classes containing 600 images per class.

How to use and download the CIFAR 10 dataset in Python?

The CIFAR 10 dataset can quickly be loaded using the open-source package Activeloop Deep Lake in Python with just one line of code. See detailed instructions on how to load the CIFAR 10 dataset training subset in Python and how to load the CIFAR 10 dataset testing subset in Python.

How can I use CIFAR 10 dataset in PyTorch or TensorFlow?

You can stream the CIFAR 10 dataset while training a model in TensorFlow or PyTorch in seconds using the Activeloop Deep Lake open-source package. See detailed instructions on how to train a model on the CIFAR 10 dataset with TensorFlow or how to train a model on CIFAR 10 dataset with PyTorch in Python.