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.

Pascal VOC 2012 Dataset

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Visualization of the Pascal VOC 2012 Dataset in the Deep Lake UI

Pascal VOC 2012 Dataset

What is Pascal VOC 2012 Dataset?

The purpose of the Pascal VOC 2012(PASCAL Visual Object Classes) dataset is to recognize objects in realistic scenarios from a variety of visual object types that are not pre-segmented objects and is basically used for the supervised learning task. The dataset can be used for different object recognition challenges such as classification, detection, segmentation, and person layout. There are a whole total of twenty object classes chosen. There are 11,530 images in the train/val data set, including 27,450 ROI-tagged objects and 6,929 segmentations.

Download Pascal VOC 2012 Dataset in Python

Instead of downloading the Pascal VOC 2012 dataset in Python, you can effortlessly load it in Python via our Deep Lake open-source with just one line of code.

Load Pascal VOC 2012 Dataset Training and validation Subset in Python

				
					import deeplake
ds = deeplake.load('hub://activeloop/pascal-voc-2012-train-val')

Load Pascal VOC 2012 Dataset Test Subset in Python

				
					import deeplake
ds = deeplake.load('hub://activeloop/pascal-voc-2012-test')

Pascal VOC 2012 Dataset Structure

Pascal VOC 2012 Data Fields

For training and validation set

images: tensor containing images.
instances/instance__mask: tensor for object segmentation.
instances/mask_pixel: tensor containing pixel values of instances of objects.
actions/box: tensor containing bounding box coordinates for action categories.
actions/key point: tensor containing a single key point for action categories.
actions/pose: tensor specifying the pose of the object. Most of the pose categories are ‘unspecified’.
actions/label: tensor containing labels that represent action categories.
actions/difficult: tensor containing the label that represents if the object is difficult to annotate.
boxes/box: tensor that contains the coordinate values of the bounding boxes.
boxes/label: tensor that contains the labels of the bounding boxes.
boxes/pose: tensor containing the numerical label that represents the index of one of the 5 pose categories.
boxes/occlusion: tensor containing the numerical label that represents if the image is occluded or not occluded.
boxes/trunc: tensor containing the numerical label that represents if the image is truncated or not truncated.
boxes/difficult: tensor containing the numerical label that represents if the image is difficult or not difficult to annotate.
parts/box: tensor that contains the coordinates of the bounding boxes for body parts used for the person layout challenge.
parts/label: tensor that contains the labels of the bounding boxes for body parts used for person layout challenge.
semantics/mask: tensor that represents the class mask.
semantics/label: tensor that contains the labels of classes present in the image and two extra labels, ‘background’ representing the background of the objects and ‘None’ representing the border/outline of the object or objects difficult to label in the image.
metadata/action_train_val_split: tensor containing details of whether the image is in train or validation split and is used or not in action detection.
metadata/segmentation_train_val_split: tensor that contains the text that gives the details of whether the image is a train split tensor contains the text ‘train’. For images used for validation contains the text ‘validation’. If the image is not used for segmentation it contains the text ‘image not used in segmentation’.
metadata/part_train_val_split: tensor that contains the text that gives the details of whether the image is used for training or validation or not used in body part/person layout detection.
metadata/main_train_val_split: tensor that contains the text that gives the details of whether the image is used for training or validation or not used in the main set.
metadata/image_meta: tensor containing image metadata.

For test set

images: tensor containing the images.
boxes/box: tensor that contains the coordinate values of the bounding boxes.
boxes/label: tensor that contains the labels of the bounding boxes.
boxes/pose: tensor containing the numerical label that represents the index of one of the 5 pose categories.
boxes/difficult: tensor containing the numerical label that represents the index if the image is difficult to annotate.
parts/box: tensor that contains the coordinates of the bounding boxes for body parts used for the person layout challenge.
parts/label: tensor that contains the labels of the bounding boxes for body parts used for the person layout challenge.
actions/box: tensor containing bounding box coordinates for action categories.
actions/key point: tensor containing a single key point for action categories.
actions/pose: tensor specifying the pose of the object. Most of the pose categories are ‘unspecified’.
actions/label: tensor containing labels that represent action categories.
actions/difficult: tensor containing the label that represents if the object is difficult to annotate.
metadata/main_test_set: tensor representing the text that gives the details of whether the image is a test set used for object classification and detection.
metadata/part_test_set: tensor representing the text that gives the details of whether the image is a test set used for a person layout challenge.
metadata/segmentation_test_set: tensor representing the text that gives the details of whether the image is a test set used for class and object segmentation.
metadata/image_meta: tensor containing image metadata.

Pascal VOC 2012 Data Splits

The Pascal VOC 2012 training and validation dataset has 17125 labeled samples.
The Pascal VOC 2012 test dataset has 5138 samples.

How to use Pascal VOC 2012 Dataset with PyTorch and TensorFlow in Python

Train a model on Pascal VOC 2012 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 Pascal VOC 2012 dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Additional Information about Pascal VOC 2012 Dataset

Pascal VOC 2012 Dataset Description

Homepage: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/
Paper: http://host.robots.ox.ac.uk/pascal/VOC/voc2012/devkit_doc.pdf

Pascal VOC 2012 Dataset Curators

Mark Everingham, John Winn

Pascal VOC 2012 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!

Pascal VOC 2012 Dataset Citation Information

				
					@article{everingham2011pascal,
title={The pascal visual object classes challenge 2012 (voc2012) development kit},
author={Everingham, Mark and Winn, John},
journal={Pattern Analysis, Statistical Modelling and Computational Learning, Tech. Rep},
volume={8},
pages={5},
year={2011}
}