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.

DRIVE Dataset

Estimated reading: 3 minutes

Visualization of the Drive train dataset in the Deep Lake UI

DRIVE Dataset

What is DRIVE Dataset?

The DRIVE (Digital Retinal Images for Vessel Extraction) dataset has been created to enable studies on retinal vessel segmentation. DRIVE dataset pictures were collected from diabetic retinopathy patients in the Netherlands. The dataset contains images from 400 diabetic patients between 25 and 90 years of age. 40 photographs have been randomly selected, with only 7 showing signs of mild early diabetic retinopathy. Automatic retinal map generation and branch point extraction were used to record time or multimedia images, and synthesize the retinal image mosaic.

Download DRIVE Dataset in Python

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

Load DRIVE Dataset Training Subset in Python

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

Load DRIVE Dataset Testing Subset in Python

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

DRIVE Dataset Structure

Data Fields

For training set:

rgb_images: tensor containing the image.
manual_masks/mask: tensor containing mask.
masks/mask: tensor containing mask.

For the testing set:

rgb_images: tensor containing the image.
masks: tensor containing mask.

DRIVE Data Splits

The DRIVE dataset training set is composed of 20 images. Only a single manual segmentation of the vasculature is provided for training images.
The DRIVE dataset test set is composed of 20 images. There are 2 manual segmentations provided where one is used as a golden standard and the other is used for comparing computer-generated segmentation with that of manual segmentation.

How to use DRIVE Dataset with PyTorch and TensorFlow in Python

Train a model on DRIVE 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 DRIVE dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

DRIVE Dataset Creation

Data Collection and Normalization Information

Images were taken using a Canon CR5 non-mydriatic 3 CCD camera with a 45-degree field of view (FOV). Each image was captured at 768 x 584 pixels using 8 bits per color plane. The FOV of each image is circular and has a diameter of approximately 540 pixels. In this database, the image is cropped around the FOV. For each image, a mask image depicting the FOV is provided.

Additional Information about DRIVE Dataset

DRIVE Dataset Description

Homepage: https://drive.grand-challenge.org/
Repository: N/A
Paper: Staal, J., Abràmoff, M. D., Niemeijer, M., Viergever, M. A., & Van Ginneken, B. (2004). Ridge-based vessel segmentation in color images of the retina. IEEE transactions on medical imaging, 23(4), 501-509.
Point of Contact: N/A

DRIVE Dataset Curators

Joes Staal, M.D. Abramoff, M. Niemeijer, M.A. Viergever, B. van Ginneken

DRIVE 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!

DRIVE Dataset Citation Information

				
					@article{staal2004ridge,
  title={Ridge-based vessel segmentation in color images of the retina},
  author={Staal, Joes and Abr{\`a}moff, Michael D and Niemeijer, Meindert and Viergever, Max A and Van Ginneken, Bram},
  journal={IEEE transactions on medical imaging},
  volume={23},
  number={4},
  pages={501--509},
  year={2004},
  publisher={IEEE}
}