UTZappos50k Dataset

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Visualization of the UTZappos dataset in the Deep Lake UI

UTZappos50k dataset

What is UTZappos50k Dataset?

UT Zappos50K (UT-Zap50K) is a huge shoe dataset that contains 50,025 catalog photos from Zappos.com. Shoes, sandals, slippers, and boots are the four primary shoe types in the dataset. The dataset also includes the functional types for each boot and specific brand names. The shoe images are centered on a white background and pictured in the same orientation for convenient analysis. GIST and LAB color features are provided in the dataset. Also, each image has eight associated meta-data (gender, materials, etc.) labels that are used to filter the shoes on Zappos.com.

Download UTZappos50k Dataset in Python

Instead of downloading the UTZappos50k dataset in Python, you can effortlessly load it in Python via our Deep Lake open-source with just one line of code.

Load UTZappos50k Dataset Training Subset in Python

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

UTZappos50k Dataset Structure

UTZappos50k Data Fields

image: tensor containing the face image.
categories: tensor to represent the footwear category.
closures: tensor to represent the type of footwear closure.
sub_categories: tensor to represent the subcategory.
in_soles: tensor to label between various insoles for footwear.
genders: tensor to represent various gender for footwear.
materials: tensor to represent the type of material for footwear.
toe_styles: tensor to distinguish different toe styles.

UTZappos50k Data Splits

The UTZappos50k dataset training set is composed of 50024.

How to use UTZappos50k Dataset with PyTorch and TensorFlow in Python

Train a model on the UTZappos50k 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 the UTZappos50k dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Additional Information about UTZappos50k Dataset

UTZappos50k Dataset Description

Homepage: https://vision.cs.utexas.edu/projects/finegrained/utzap50k/
Repository: N/A
Paper: A. Yu and K. Grauman. “Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images”. In ICCV, 2017.
Point of Contact: N/A

UTZappos50k Dataset Curators

A. Yu and K. Grauman

UTZappos50k 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!

UTZappos50k Dataset Citation Information

				
					@inproceedings{,
  title = {Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images},
  author = {A. Yu and K. Grauman},
  booktitle = {ICCV},
  year = {2017} 
}

UTZappos50k Dataset FAQs

What is the UTZappos50k dataset for Python?

The UT Zappos50K (UT-Zap50K) dataset contains 50,025 catalog photos from Zappos.com. Each image in the dataset has eight associated meta-data (gender, materials, etc.) labels that are used to filter the shoes on Zappos.com. Shoes, sandals, slippers, and boots are the four primary categories, followed by functional types and specific brands.

How can I use UTZappos50k in PyTorch or TensorFlow?

You can stream the UTZappos50k 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 UTZappos50k dataset with PyTorch in Python or train a model on the UTZappos50k dataset with TensorFlow in Python.