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.

CelebA Dataset

Estimated reading: 6 minutes

Visualization of the Celeb-A dataset in the Deep Lake UI

Celeb-A dataset

What is Celeb-A Dataset?

The CelebFaces Attributes Dataset (CelebA) consists of more than 200K celebrity images with 40 attribute annotations each. The images range from extreme poses to heavily background-cluttered backgrounds. Images cover large pose variations, background clutter, and diverse people, making this dataset great for training and testing models for face detection. It can identify people with brown hair, smiling, or wearing glasses.

Download Celeb-A Dataset in Python

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

Load CelebA Dataset Training Subset in Python

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

Load CelebA Dataset Validation Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/celeb-a-val")

Load CelebA Dataset Testing Subset in Python

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

CelebA Dataset Structure

CelebA Data Fields

image: tensor containing the 178×218 image.
bbox: tensor containing bounding box of their respective images.
keypoints: tensor to identify 63 various key points from face
clock_shadow: tensor to check cloak shadow.
arched_eyebrows: tensor to check arch eyebrows.
attractive: tensor to check if attractive or not.
bags_under_eyes: tensor to check if bags are under the eyes.
bald: tensor to check if bald or not.
bangs: tensor to check if bangs are there or not.
big_lips: tensor to check if big lips are there or not.
big_nose: tensor to check if big nose is there or not.
black_hair: tensor to check the presence of black hair.
blond_hair: tensor to check if blond hair or not.
blurry: tensor to check if the image is blurred.
brown_hair: tensor to check the presence of brown hair.
bushy_eyebrows: tensor to check the presence of bushy eyebrows.
chubby: tensor to check if chubby or not.
double_chin: tensor to check the presence of double chin.
eyeglasses: tensor checks the presence of eyebrows.
goatee: tensor to check the presence of a goatee in a person.
gray_hair: tensor to check the presence of gray hair.
heavy_makeup: tensor to check the presence of heavy makeup.
high_cheekbones: tensor to check the presence of high cheekbones.
male: tensor to check if the person is male.
mouth_slightly_open: tensor to check if the mouth is open.
mustache: tensor to check the presence of a mustache.
narrow_eyes: tensor to check narrow eyes or not.
no_beard: tensor to check if the beard is present.
oval_face: tensor to check if the face is oval.
pale_skin: tensor to check if the skin is pale.
pointy_nose: tensor to check if the nose is pointy.
receding_hairline: tensor to check if the hairline is receding.
rosy_cheeks: tensor to check if the cheeks are rosy.
sideburns: tensor to check the presence of sideburns.
smiling: tensor to check if the person is smiling.
straight_hair: tensor to check if the hair is straight.
wavy_hair: tensor to check if the hair is wavy.
wearing_earrings: tensor to check the presence of earing.
wearing_hat: tensor to check the presence of the hat.
wearing_lipstick: tensor to check the presence of lipstick.
wearing_necklace: tensor to check the presence of the necklace.
wearing_necktie: tensor to check the presence of necktie.
young: tensor to check if the person is young.

CelebA Data Splits

The CelebA dataset training set is composed of 162,770.
The CelebA dataset test set was composed of 19,962.
The CelebA dataset val set was composed of 19,867.

How to use CelebA Dataset with PyTorch and TensorFlow in Python

Train a model on CelebA 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 CelebA dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Additional Information about CelebA Dataset

CelebA Dataset Description

Homepage: https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
Repository: N/A
Paper: Liu, Ziwei and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou: Deep Learning Face Attributes in the Wild, Proceedings of International Conference on Computer Vision (ICCV), 2015
Point of Contact: ziwei.liu at ntu.edu.sg

CelebA Dataset Curators

Liu, Ziwei and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou

CelebA 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!

CelebA Dataset Citation Information

				
					@inproceedings{liu2015faceattributes,
  title = {Deep Learning Face Attributes in the Wild},
  author = {Liu, Ziwei and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou},
  booktitle = {Proceedings of International Conference on Computer Vision (ICCV)},
  month = {December},
  year = {2015} 
}

CelebA Dataset FAQs

What is the CelebA dataset for Python?

What is the CelebA dataset used for?

This dataset is great for training and testing models for face detection, particularly for recognizing facial attributes such as finding people with brown hair, smiling, or wearing glasses. Images cover large pose variations, background clutter, and diverse people, supported by a large number of images and rich annotations.

How can I use CelebA dataset in PyTorch or TensorFlow?

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