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.

SQuAD Dataset

Estimated reading: 3 minutes

What is SQuAD Dataset?

The SQuAD(Stanford Question Answering Dataset) dataset consists of 100,000 questions with over 50,000 unanswerable questions and additionally assesses a system’s capacity to not only answer reading comprehension questions but also to refrain from answering a question that cannot be answered based on the given paragraph.

Download SQuAD Dataset in Python

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

Load SQuAD Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load('hub://activeloop/squad-train')

Load SQuAD Dataset Validation Subset in Python

				
					import deeplake
ds = deeplake.load('hub://activeloop/squad-val')

SQuAD Dataset Structure

SQuAD Data Fields

id : tensor containing article id.
title : tensor containing the title.
context: tensor containing a context from the article.
question : tensor containg the question
text : tensor containing the text
answer_start : tensor that contains the starting index of the answer.
is_impossible: A label that represents whether answering is possible or impossible.If it is impossible, label represents the value 1 for True. If not impossible label respresents the value o for False.

SQuAD Data Splits

The SQuAD dataset training set is composed of 86821 samples.
The SQuAD dataset validation set is composed of 20302 samples.

How to use SQuAD Dataset with PyTorch and TensorFlow in Python

Train a model on SQuAD 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 SQuAD dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

SQuAD Dataset Creation

Data Collection and Normalization Information

Up to five questions that were impossible to answer based on each paragraph alone were written with the help of crowd workers, referring entities in the paragraph and ensuring that a credible response could be found from the entire SQuAD 1.1 text. Those questions from workers who didn’t understand the task in that article were screened to reduce noise. The train, development, and test splits were constructed using the same article division as SQuAD 1.1, with each split integrating old and new data and eliminating articles containing unanswerable questions. For SQuAD 2.0, the ratio of answerable to unanswerable questions in these splits is around one-to-one, whereas train data has roughly twice as many answerable as unanswerable questions.

Additional Information about SQuAD Dataset

SQuAD Dataset Description

Homepage: https://rajpurkar.github.io/SQuAD-explorer/
Paper: https://arxiv.org/pdf/1806.03822.pdf
Point of Contact: robinjia@stanford.edu, https://groups.google.com/forum/#!forum/squad-stanford-qa

SQuAD Dataset Curators

Pranav Rajpurkar, Robin Jia, Percy Liang

SQuAD Dataset Licensing Information

CC BY-SA 4.0

SQuAD Dataset Citation Information

				
					@article{rajpurkar2018know,
title={Know what you don't know: Unanswerable questions for SQuAD},
author={Rajpurkar, Pranav and Jia, Robin and Liang, Percy},
journal={arXiv preprint arXiv:1806.03822},
year={2018}
}