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300w Dataset

Estimated reading: 3 minutes

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.

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