How to download a machine learning dataset for PyTorch and TensorFlow in Python

Without any need to download, a variety of popular machine learning datasets can be accessed and streamed with Hub with one line of code. This enables you to explore the datasets and train models without needing to download machine learning datasets regardless of their size. Access classical datasets like CIFAR-10, MNIST or Fashion-MNIST, as well as large datasets like Google Objectron, ImageNet, COCO, and many others in Python.

What is MNIST Dataset?

The MNIST (Modified National Institute of Standards and Technology database) dataset contains a training set of 60,000 images and a test set of 10,000 images of handwritten digits. The handwritten digit images have been size-normalized and centered in a fixed size of 28×28 pixels. The MNIST digits dataset is often used by data scientists who want to try machine learning techniques and pattern recognition methods on real-world data while spending minimal effort on preprocessing and formatting.

What is COCO Dataset?

The COCO (Common Objects in Context) dataset comprises 91 common object categories, 82 of which have more than 5,000 labeled examples. The dataset has 2.5 million labeled instances in 328k photos, created with the help of a large number of crowd workers using unique user interfaces for category detection, instance spotting, and instance segmentation. COCO dataset is a novel dataset with the purpose of improving the state-of-the-art in object detection by aligning the problem within the larger framework of scene understanding.

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.

What is CIFAR 100 Dataset?

The CIFAR100 (Canadian Institute For Advanced Research) dataset consists of 100 classes with 600 color images of 32×32 resolution for each class. It is divided into 500 training and 100 testing images per class. In CIFAR100, there are 20 superclasses sub-grouped into 100 classes. The dataset comes with two labels for each image such as a “fine” label (class) and a “coarse” label (superclass).

What is the MNIST dataset used for

MNIST is used as a “hello world” example by data scientists worldwide. Typically, MNIST dataset is used as a benchmark dataset, or as a proof-of-concept for training and testing purposes in the field of machine learning.

What is the MNIST dataset for Python?

The MNIST dataset (Modified National Institute of Standards and Technology database) is one of the most popular datasets in machine learning. MNIST is a dataset of 60,000 square 28×28 pixel images of handwritten single digits between 0 and 9. The images are in grayscale format.

How to download the MNIST dataset in Python?

You can load MNIST dataset fast with one line of code using the open-source package Activeloop Hub in Python. See detailed instructions on how to load MNIST dataset training subset or MNIST dataset testing subset in Python.

How to create an Image Dataset like MNIST dataset?

With Activeloop Hub, creating image datasets like the MNIST digits dataset is simple. Simple datasets like MNIST can be created automatically by allowing Hub parse the legacy files into Hub dataset format. More complex datasets can be created manually.

What is the size of each image in the MNIST dataset?

MNIST dataset image size is constant across all images of the dataset. Each MNIST dataset image is a fixed-size 28×28 pixel square image.

What does MNIST stand for?

MNIST is an acronym for the Modified National Institute of Standards and Technology dataset.

Why is MNIST so popular?

Created in 1998, MNIST dataset containing handwritten digits from 0-9 is small in size, simple in structure and easy to use. This makes it a good first dataset to try various pattern recognition methods on, with minimal effort required on data preprocessing..

Is MNIST suitable for deep learning?

Yes, MNIST data set is one of the most used datasets for deep learning. As of July 2022, MNIST only falls behind CIFAR-10, Imagenet, coco datasets in the amount of papers that cite them (or has used as a benchmark dataset.

KMNIST

Estimated reading: 5 minutes

Visualization of the KMNIST Test Dataset in the Deep Lake UI

KMNIST dataset

What is KMNIST Dataset?

The KMNIST (Kuzushiji-MNIST) dataset is a drop-in replacement for the MNIST dataset, and is comprised of a training set of 60,000 examples and a testing set of 10,000 examples of handwritten Kuzushiji (cursive Japanese) Hiragana characters. The handwritten characters have been processed to fit into 28×28 pixel resolution grayscale images. The KMNIST dataset is suggested for data scientists who want to try machine learning techniques and computer vision techniques on real-world data while spending minimal effort on preprocessing and formatting.

Download KMNIST Dataset in Python

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

Load KMNIST Dataset Training Subset in Python

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

Load KMNIST Dataset Testing Subset in Python

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

KMNIST Dataset Structure

KMNIST Data Fields

images: tensor containing the 28×28 images.
labels: one of ten possible classes, each representing one row of Hiragana.

KMNIST Data Splits

The KMNIST dataset training set is composed of 60,000 images, perfectly balanced between the ten classes.
The KMNIST dataset test set is composed of 10,000 images, perfectly balanced between the ten classes.

How to use KMNIST Dataset with PyTorch and TensorFlow in Python

Train a model on the KMNIST dataset with PyTorch

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 the KMNIST dataset with TensorFlow

				
					dataloader = ds.tensorflow()

KMNIST Dataset Creation

Data Collection and Normalization Information

Source books (35 classical Japanese books from the 18th century) were scanned, and a bounding box was created for each character. The bounding box images were then processed to conform to the 28×28 pixel resolution in grayscale.

Additional Information about KMNIST Dataset

KMNIST Dataset Description

Homepage: http://codh.rois.ac.jp/kmnist/index.html.en
Repository: https://github.com/rois-codh/kmnist
Paper: T. Clanuwat, M. Bober-Irizar, A. Kitamoto, A. Lamb, K. Yamamoto and D. Ha: Deep Learning for Classical Japanese Literature, arXiv:1812.01718, December 2018
Point of Contact: https://github.com/rois-codh/kmnist/issues

KMNIST Dataset Curators

ROIS-DS Center for Open Data in the Humanities (CODH), adapted from "Kuzushiji Dataset" (created by National Institute of Japanese Literature and others).

KMNIST Dataset Licensing Information

Creative Commons Attribution Share-Alike 4.0 International license

KMNIST Dataset Citation Information

				
					@online{clanuwat2018deep,
  author       = {Tarin Clanuwat and Mikel Bober-Irizar and Asanobu Kitamoto and Alex Lamb and Kazuaki Yamamoto and David Ha},
  title        = {Deep Learning for Classical Japanese Literature},
  date         = {2018-12-03},
  year         = {2018},
  eprintclass  = {cs.CV},
  eprinttype   = {arXiv},
  eprint       = {cs.CV/1812.01718},
}

KMNIST Dataset FAQs

What is the KMNIST dataset for Python?

The KMNIST dataset (Kuzushiji-MNIST) is a drop-in replacement for the MNIST (Modified National Institute of Standards and Technology) dataset, which is one of the most well-known datasets in machine learning. KMNIST is a dataset of 70,000 (60,000 training examples and 10,000 testing examples) 28×28 images of handwritten single Kuzushiji (cursive Japanese) Hiragana characters from classical Japanese literature, spanning ten classes representing the corresponding rows in modern Hiragana. Similar to MNIST, the images are in grayscale format. The classes are as follows:

Class / Codepoint
Hiragana Character

U+304A
お

U+304D
き

U+3059
す

U+3064
つ

U+306A
な

U+306F
は

U+307E
ま

U+3084
や

U+308C
れ

U+3092
を
 

What is the KMNIST dataset used for?

KMNIST is considered a more difficult training task than MNIST due to the many-to-one mapping between labels and characters. This is the case as one class in KMNIST may have many characters mapped to it. KMNIST can be used as a more challenging machine learning task after working with the MNIST dataset

How to download the KMNIST dataset in Python?

You can load the KMNIST dataset fast with one line of Python code using the open-source package Activeloop Deep Lake. See detailed instructions on how to load the KMNIST dataset training subset or how to load the KMNIST dataset testing subset.

How can I use the KMNIST dataset in PyTorch or TensorFlow?

You can stream KMNIST dataset while training a model in PyTorch or TensorFlow with one line of code using the open-source package Activeloop Deep Lake in Python. See detailed instructions on how to train a model on MNIST dataset with PyTorch or train a model on MNIST dataset with TensorFlow .

Should I work with KMNIST dataset in CSV?

No. CSV is not optimized for working with image data, especially for machine learning workflows. Instead of downloading KMNIST dataset in CSV format, you easily load, version-control, query, and manipulate KMNIST for machine learning purposes using Activeloop Deep Lake.

How to create an Image Dataset like KMNIST dataset?

With Activeloop Deep Lake, creating image datasets like the MNIST digits dataset is easy. Simple datasets like KMNIST can be created automatically by allowing Deep Lake to parse the legacy files into Deep Lake dataset format. More complex datasets can be created manually.

KMNIST vs Fashion-MNIST. What is the difference between KMNIST and Fashion-MNIST?

KMNIST and Fashion-MNIST dataset are two separate datasets.Both datasets are inspired by, and meant to be alternatives to the original MNIST dataset, and are thus of the same size (60,000 28×28 grayscale examples in the training set, and 10,000 28×28 grayscale examples in the testing set) and have the same number of classes.

Fashion-MNIST comprises pictures of clothing items and was published in 2017 by Zalando, a German online retailer. KMNIST comprises of handwritten Hiragana characters from classical Japanese books, and was published in 2018. It was curated by the ROIS-DS Center for Open Data in the Humanities (CODH).

What is the size of each image in the KMNIST dataset?

Each image in the KMNIST dataset is a 28×28 grayscale image.