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.

FFHQ Dataset

Estimated reading: 4 minutes

Visualization of the FFHQ Dataset in the Deep Lake UI

FFHQ (Flickr-Faces-HQ) Dataset

What is FFHQ Dataset?

The FFHQ Dataset (Flickr-Faces-HQ) dataset is a high quality image set containing 70,000 PNG images at 1024×1024 resolution, its been put a considerable effort to include as many attributes as possible and variations on these, so expect a wide range of different age, and ethinicty.

Importantly, this dataset is not intended for, and should not be used for, development or improvement of facial recognition technologies.

Download FFHQ Dataset in Python

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

Load FFHQ Dataset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/ffhq")

FFHQ Dataset Structure

FFHQ Data Fields

images_1024/face_landmarks: an int32 tensor containing 204 coco points
images_1024/image: a uint8 tensor containing a 1024 x 1024 png image
images_128/image: a uint8 tensor containing a 128 x 128 png image
images_metadata: an str tensor containing the 70,000 image metadata
images_wild/face_landmarks: a int32 tensor containing 204 coco points
images_wild/face_quad: a float32 tensor containing 4 generic objects
images_wild/face_rect: a int32 tensor containing 1 bbox object
images_wild/image: a uint8 tensor containing a 1062 x 1072 compressed png image

FFHQ Data Splits

				
					import deeplake
ds_train = deeplake.load("hub://activeloop/ffhq")[60000]
ds_val = deeplake.load("hub://activeloop/ffhq")[10000]

How to use FFHQ Dataset with PyTorch and TensorFlow in Python

Train a model on FFHQ 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 FFHQ dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

FFHQ Dataset Creation

Data collection and Normalization of images

These images were collected from Flickr under a permissive license, they were cropped and aligned using dlib and various filters were used to prune the set. Finally, Amazon Mechanical Turk was used to remove the occasional statues, paintings, or photos of photos.

Additional information about FFHQ Dataset

FFHQ Dataset Description

Homepage: N/A
Repository: https://github.com/NVlabs/ffhq-dataset
Paper: T. Karras, S. Laine and T. Aila, “A Style-Based Generator Architecture for Generative Adversarial Networks,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 4396-4405, doi: 10.1109/CVPR.2019.00453.
Point of Contact: researchinquiries@nvidia.com

FFHQ Dataset Curators

Terros Karras, Samuli Laine, Timo Aila

FFHQ Dataset Licensing information

Creative Commons Attribution-Share Alike 4.0 license

FFHQ Dataset Citation Information

				
					@article{,
  title={A Style-Based Generator Architecture for Generative Adversarial Networks},
  author={Tero Karras, Samuli Laine, Timo Aila},
  journal={IEEE[Online]. Avaliable: https://ieeexplore.ieee.org/document/8953766},
  volume={3},
  year={2019}
}

FFHQ Datasets FAQs

What is the FFHQ dataset for Python?

The FFHQ dataset(Flickr-Faces-HQ) is a popular high quality image dataset scraped from twitter. FFHQ dataset provides 70,000 PNG images of a wide range of people of different features, these images are provided with a resolution of 1024 x 1024.

What is the FFHQ dataset used for?

FFHQ is used as a means for the benchmarking of generative adversarial networks models, but the wide range of age and other features allows for multiple uses on this dataset such as face aging, face generation, and metric learning.

How to download the FFHQ dataset in Python?

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

How can i use FFHQ dataset in Pytorch or TensorFlow?

You can stream the FFHQ 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 FFHQ dataset with Pytorch in Python or train a model on FFHQ dataset with TensorFlow in Python.