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.

SWAG Dataset

Estimated reading: 5 minutes

SWAG dataset

What is SWAG Dataset?

The SWAG (Situations With Adversarial Generations) dataset comprises 113,000 multiple-choice questions covering a wide range of grounded scenarios and is taken from two consecutive video captions in the ActivityNet Captions database, as well as the Large Scale Movie Description Challenge. This dataset is created using adversarial filtering. SWAG dataset can be used in the research toward commonsense NLI.

Download SWAG Dataset in Python

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

Load SWAG Dataset Training Subset in Python

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

Load SWAG Dataset Testing Subset in Python

				
					import deeplake
ds = deeplake.load('hub://activeloop/swag-test')

Load SWAG Dataset Validation Subset in Python

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

SWAG Dataset Structure

SWAG Data Fields

For the training and validation set

video_id: tensor that contains video id.
fold_ind: tensor that contains fold id.
start_phrase: tensor containing the start phrase of the context.
gold_ending: tensor containing better ending.
distractor_0: tensor containing the first distractor. The answer with the first distractor is considered to have the best quality.
distractor_1: tensor containing the second distractor.
distractor_2: tensor containing the third distractor.
distractor_3: tensor containing the fourth distractor. The answer with the fourth distractor is considered to have the lowest quality.
gold_source: tensor containing labels gold and gen. gen indicates generated best answer and gold indicates the real answer which is considered as the second best.
gold_type: label containing values ‘pos’ and ‘unl’
distractor_0_type: label containing values ‘pos’ and ‘unl’
distractor_1_type: label containing values ‘pos’ and ‘unl’
distractor_2_type: label containing values ‘pos’ and ‘unl’
distractor_3_type: label containing values ‘n/a’, ‘pos’ and ‘unl’
sentence_1: tensor containing the first sentence.
sentence_2: tensor containing the second sentence.

For the test set

video_id: tensor that contains video id.
fold_ind: tensor that contains fold id.
start_phrase: tensor containing the start phrase of the context.
gold_source: tensor containing labels gold and gen. gen indicates generated best answer and gold indicates the real answer which is considered as the second best.
ending0: tensor containing first ending.
ending1: tensor containing second ending.
ending2: tensor containing third ending.
ending3: tensor containing fourth ending.
sentence_1: tensor containing the first sentence.
sentence_2: tensor containing the second sentence

SWAG Data Splits

The SWAG dataset train set was composed of 73,000 multiple-choice questions about grounded situations.
The SWAG dataset validation set was composed of 20,000 multiple-choice questions about grounded situations.
The SWAG dataset test set was composed of 20,000 multiple choice questions about grounded situations for the (blind) test.

How to use SWAG Dataset with PyTorch and TensorFlow in Python

Train a model on SWAG 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 SWAG dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

SWAG Dataset Creation

Source Data

Data Collection and Normalization Information

The dataset was created by taking two consecutive video captions from the ActivityNet Captions website and the LSMD challenge. These datasets differ somewhat in nature and allow us to attain larger coverage. A constituency parser is utilized to break the second sentence into noun and verb phrases for every pair of captions. Each question has a gold manually-validated ending and three distractors.

Additional Information about SWAG Dataset

SWAG Dataset Description

Homepage: https://rowanzellers.com/swag/
Repository: https://github.com/rowanz/swagaf/tree/master/data
Paper: Rowan Zellers, Yonatan Bisk, Roy Schwartz, Yejin Choi: Swag: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference
Point of Contact: https://rowanzellers.com/#contact

SWAG Dataset Curators

Rowan Zellers, Yonatan Bisk, Roy Schwartz, Yejin Choi

SWAG Dataset Licensing Information

MIT Licence

SWAG Dataset Citation Information

				
					@inproceedings{zellers2018swagaf,
    title={SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference},
    author={Zellers, Rowan and Bisk, Yonatan and Schwartz, Roy and Choi, Yejin},
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    year={2018}
}

SWAG Dataset FAQs

What is the SWAG dataset for Python?

The SWAG dataset (Situations With Adversarial Generations) is made up of 113,000 multiple-choice questions about grounded situations. It is a large-scale dataset for the tasks of grounded commonsense inference, unifying natural language inference, and physically grounded reasoning.

What is the SWAG dataset used for?

The SWAG dataset is used to train NLP models that can handle multiple choice questions.

How to download the SWAG dataset in Python?

Load the SWAG dataset with one line of code using Activeloop Deep Lake the open-source package made in Python. Check out detailed instructions on how to load the SWAG dataset training subset in Python, load the SWAG dataset testing subset in Python, and load the SWAG dataset validation subset in Python.

How can I use SWAG dataset in PyTorch or TensorFlow?

You can train a model on SWAG dataset with PyTorch in Python or train a model on the SWAG dataset with TensorFlow in Python. You can stream the SWAG dataset while training a model in PyTorch or TensorFlow with one line of code using the open-source package Activeloop Deep Lake that is written in Python.