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.

Office-Home Dataset

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Visualization of the office-home dataset in the Deep Lake UI

Office-Home Dataset

What is Office-Home Dataset?

The Office-Home dataset was created to assess deep learning algorithms for domain adaptation-based object recognition. The dataset consists of images from 4 different domains which include art, clip art, product, and Real-World images. The dataset contains images of 65 types of objects commonly found in Office-Home Settings.

Download Office-Home Dataset in Python

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

Load Office-Home Dataset in Python

import deeplake
ds = deeplake.load('hub://activeloop/office-home-domain-adaptation')

Office-Home Dataset Structure

Office-Home Data Fields

images: tensor containing images
domain_objects: labels that represent 65 categories of objects in each domain
domain_categories: labels that represent 4 domain categories

How to use Office-Home Dataset with PyTorch and TensorFlow in Python

Train a model on Office-Home 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 Office-Home Dataset with TensorFlow in Python

dataloader = ds.tensorflow()

Office-Home Dataset Creation

Data Collection and Normalization Information

Python crawler was used for image collection. There were 100,000 images of 120 different objects. To make sure that the right objects are present in the image, the dataset was cleaned. It was also ensured that each category has a certain number of images. The last version of the dataset has 15,500 images of 65 different objects.

Additional Information about Office-Home Dataset

Office-Home Dataset Description

Homepage: https://www.hemanthdv.org/officeHomeDataset.html
Repository: N/A
Paper: https://openaccess.thecvf.com/content_cvpr_2017/papers/Venkateswara_Deep_Hashing_
Network_CVPR_2017_paper.pdf
Point of Contact: N/A

Office-Home Dataset Curators

Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty and Sethuraman Panchanathan

Office-Home Dataset Licensing Information

More information about the license can be found here. 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!

Office-Home Dataset Citation Information

@inproceedings{venkateswara2017deep,
title={Deep hashing network for unsupervised domain adaptation},
author={Venkateswara, Hemanth and Eusebio, Jose and Chakraborty, Shayok and Panchanathan, Sethuraman},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={5018--5027},
year={2017} }

Office-Home Dataset FAQs

What is the Office-Home dataset for Python?

The Office-Home dataset was developed to assess domain adaptation algorithms for object recognition using deep learning. The dataset is made up of images from four different domains—artistic, product, real-world images, and clip art. A Python web crawler that crawled through several search engines and online image directories was used to collect the images in the dataset.

What is the Office-Home dataset used for?

The Office-Home dataset is used as a benchmark dataset for domain adaptation. It contains four domains and each domain consists of 65 categories. The four domains include art (a collection of artistic images in the form of sketches), clipart (a collection of clipart images), product (a domain containing images of objects without a background), and real-world images (a domain containing images of objects captured with a regular camera).

How to download the Office-Home dataset in Python?

With the open-source package Activeloop Deep Lake in Python you can load the Office-Home dataset fast with one line of code. See detailed instructions on how to load the Office-Home dataset in Python.

How can I use Office-Home dataset in PyTorch or TensorFlow?

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