300w Dataset

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Visualization of the 300w train datasets in the Deep Lake UI

300w dataset

What is 300w Dataset?

The 300w is a face dataset made up of 300 in-the-wild photographs taken inside and outdoors. It encompasses a wide range of identity, expression, lighting, stance, occlusion, and facial size. The photographs were obtained by searching for “party,” “conference,” “protests,” “football,” and “celebrities” on Google. The 300w database has a higher percentage of partially-occluded photos and covers more expressions than the standard “neutral” or “smile,” such as “surprise” or “scream,” when compared to other in-the-wild datasets. A semi-automatic approach was used to annotate images using the 68-point markup.

Download 300w Dataset in Python

Instead of downloading the 300w dataset in Python, you can effortlessly load it in Python via our Deep Lake open-source with just one line of code.

Load 300w Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/300w")

300w Dataset Structure

300w Data Fields

image: tensor containing the face image.
keypoints: tensor to identify various key points from the face.
labels: tensor to distinguish between indoor and outdoor images.

300w Data Splits

The 300w dataset training set is composed of 599 images.

How to use 300w Dataset with PyTorch and TensorFlow in Python

Train a model on 300w 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 a 300w dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Additional Information about 300w Dataset

300w Dataset Description

Homepage: https://ibug.doc.ic.ac.uk/resources/300-W/
Repository: N/A
Paper: C. Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou, M. Pantic. 300 faces In-the-wild challenge: Database and results. Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation “In-The-Wild”. 2016.
Point of Contact: [email protected]

300w Dataset Curators

C. Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou, M. Pantic

300w 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!

300w Dataset Citation Information

				
					@inproceedings{,
  title = { 300 faces In-the-wild challenge: Database and results},
  author = {C. Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou, M. Pantic},
  booktitle = {Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation},
  year = {2016} 
}

300w Dataset FAQs

What is the 300w dataset for Python?

The 300w dataset is a face dataset comprised of 300 in-the-wild photos taken inside and outside. It includes a wide scope of personality, appearance, lighting, position, impediment, and facial size.

How to download the 300w dataset in Python?

You can load a 300w dataset fast with one line of code using the open-source package Activeloop Deep Lake in Python. See detailed instructions on how to load a 300w dataset training subset in Python.

How can I use 300w dataset in PyTorch or TensorFlow?

You can stream a 300w 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 a 300w dataset with PyTorch in Python or train a model on a 300w dataset with TensorFlow in Python.