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.

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

MNIST and Fashion-MNIST dataset are two separate datasets. However, the Fashion-MNIST dataset is meant to be an MNIST dataset alternative. Fashion-MNIST comprises pictures of clothing items and was published in 2017 by Zalando, a German online retailer. Both datasets are of the same size: 60 000 photos in the training set, as well as 10 000 pictures of clothing in the validation set of the dataset.

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.

NSynth Dataset

Estimated reading: 4 minutes

Visualization of the NSynth dataset in the Deep Lake UI

NSynth dataset

What is NSynth Dataset?

NSynth is a dataset made of one-shot instrumental notes. The dataset contains 305,979 musical notes that have a unique pitch, timbre, and envelope. The sounds in the dataset were collected from 1006 instruments from commercial sample libraries. The sounds were annotated based on their sonic qualities, source (acoustic, electronic, or synthetic), and instrument family (bass, brass, flute, guitar, keyboard, mallet, organ, reed, string, synth lead, and vocal).

Download NSynth Dataset in Python

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

Load NSynth Dataset Training Subset in Python

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

Load NSynth Dataset Testing Subset in Python

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

Load NSynth Dataset Validation Subset in Python

import deeplake
ds = deeplake.load("hub://activeloop/nsynth-val")

NSynth Dataset Structure

NSynth Data Fields

audios: tensor to represent audio in wav format.
sample_rate: tensor representing the sample rate of audio.
pitch: tensor representing the pitch of audio.
instrument_source: tensor representing the source of audio instrument.
instrument_family: tensor to represent the family of audio instrument.
note: tensor text representing the audio.
qualities: tensor to represent the quality of audio.
velocity: tensor to represent the velocity of audio wave.
instrument: tensor to represent the instrument with which the audio is created.

NSynth Data Splits

The NSynth dataset training set is composed of 289205.
The NSynth dataset testing set is composed of 4096.
The NSynth dataset validation set is composed of 12678.

How to use NSynth Dataset with PyTorch and TensorFlow in Python

Train a model on NSynth 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 NSynth dataset with TensorFlow in Python

dataloader = ds.tensorflow()

Additional Information about EMNIST Dataset

NSynth Dataset Description

Homepage: https://magenta.tensorflow.org/datasets/nsynth
Repository: N/A
Paper: Jesse Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Douglas Eck, Karen Simonyan, and Mohammad Norouzi. “Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders.” 2017.
Point of Contact: N/A

NSynth Dataset Curators

Jesse Engel, Cinjon Resnick, Adam Roberts, Sander Dieleman, Douglas Eck, Karen Simonyan, and Mohammad Norouzi

NSynth 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!

NSynth Dataset Citation Information

@misc{nsynth2017,
    Author = {Jesse Engel and Cinjon Resnick and Adam Roberts and
              Sander Dieleman and Douglas Eck and Karen Simonyan and
              Mohammad Norouzi},
    Title = {Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders},
    Year = {2017},
    Eprint = {arXiv:1704.01279},
}

NSynth Dataset FAQs

What is the NSynth dataset for Python?

NSynth is an audio dataset with 305,979 musical notes. The dataset is often used to classify wave files (.wav) based on their instrument family. NSynth is also a popular benchmark and entry point into audio machine learning. The creators of the dataset view NSynth as a building block for future datasets.

How can I use NSynth dataset in PyTorch or TensorFlow?

You can stream the NSynth 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 NSynth dataset with PyTorch in Python or train a model on NSynth dataset with TensorFlow in Python.