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.

WIDER Face Dataset

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Visualization of Wider face dataset in Deep Lake UI

WIDER Face dataset

What is WIDER Face Dataset?

The WIDER FACE dataset is a face detection benchmark dataset. The images in this dataset were selected from the publicly available WIDER dataset. The WIDER FACE dataset was organized based on 61 event classes. For each event class, data such as training, validation, and testing were randomly selected from the WIDER dataset. Similar to MALF and Caltech datasets, the WIDER FACE does not release the bounding box ground truth for the test images.

Download WIDER FACE Dataset in Python

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

Load WIDER Face Dataset Training Subset in Python

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

Load WIDER Face Dataset Validation Subset in Python

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

Load WIDER Face Dataset Testing Subset in Python

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

WIDER Face Dataset Structure

WIDER Face Data Fields

image: tensor containing the face image.
boxes: tensor representing bounding boxes.
poses: tensor to distinguish types of poses.
expressions: tensor to distinguish between ‘exaggerate_expression’ and ‘typical expression’.
illuminations: tensor to distinguish between ‘normal illumination’ and ‘extreme illumination’.
occlusions: tensor to distinguish ‘no occlusion’, ‘partial occlusion’, and ‘heavy occlusion’
validities: tensor checks if the image is valid or invalid.
blurs: tensor to distinguish ‘clear’, ‘normal blur’, and ‘heavy blur’ images.

WIDER Face Splits

The WIDER Face dataset training set is composed of 12880.
The WIDER Face dataset test set was composed of 3226.
The WIDER Face dataset val set was composed of 16097.

How to use WIDER Face Dataset with PyTorch and TensorFlow in Python

Train a model on WIDER Face 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 WIDER Face dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Additional Information about WIDER Face Dataset

WIDER Face Dataset Description

Homepage: http://shuoyang1213.me/WIDERFACE/
Paper: Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou in WIDER FACE: A Face Detection Benchmark
Point of Contact: shuoyang.1213@gmail.com

WIDER Face Dataset Curators

Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou

WIDER Face 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!

WIDER Face Citation Information

				
					@inproceedings{yang2016wider,
    Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou},
    Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    Title = {WIDER FACE: A Face Detection Benchmark},
    Year = {2016} }

WIDER Face Dataset FAQs

What is the WIDER Facedataset for Python?

The WIDER FACE dataset is commonly used as a Face Detection Benchmark. it contains 32,203 images and labels 393,703 faces with a high degree of variability in scale, poses, and occlusion. The database is split into training (40%), validation (10%), and testing (50%) sets. The images in the dataset are divided into three levels (easy, medium, and hard) according to the difficulties of image detection.

How to download the WIDER Face dataset in Python?

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

How can I use WIDER Face dataset in PyTorch or TensorFlow?

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