FFHQ Dataset

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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, it has been put considerable effort to include as many attributes as possible and variations these, so expect a wide range of different ages and ethnicity.

Importantly, this dataset is not intended for, and should not be used for, the 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 box 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 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: [email protected]

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 network 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 the FFHQ dataset with Pytorch in Python or train a model on the FFHQ dataset with TensorFlow in Python.