LSP Dataset

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Visualization of the LSP train dataset in the Deep Lake UI

LSP Dataset

What is LSP Dataset?

The Leeds Sports Pose (LSP) dataset is an image dataset. It is widely used as the benchmark for human pose estimation. The LSP dataset contains 10,000 images gathered from Flickr searches for the tags “parkour”, “gymnastics”, and “athletics”. The images in the dataset consist of poses deemed to be challenging to estimate. Each image in the dataset has been scaled such that the annotated person is roughly 150 pixels in length. Also, each image has been annotated with up to 14 visible joint locations.

Download LSP Dataset in Python

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

Load LSP Dataset Training Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/lsp-train")

Load LSP Dataset Testing Subset in Python

				
					import deeplake
ds = deeplake.load("hub://activeloop/lsp-test")

LSP Dataset Structure

LSP Data Fields

images: tensor containing images of the dataset.

keypoints: tensor containing keypoints in the format x,y, visibility. x and y are multiplied with 1.75 for visualization of key points on Activeloop Deep Lake.

images_visualized: tensor containing images the way they will be visualized after training.

LSP Data Splits

The LSP dataset training set is composed of 1000 images with 14 key points each.

The LSP dataset testing set is composed of the rest of the 1000 images with 14 key points each.

How to use LSP Dataset with PyTorch and TensorFlow in Python

Train a model on LSP 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 LSP dataset with TensorFlow in Python

				
					dataloader = ds.tensorflow()

Additional Information about LSP Dataset

LSP Dataset Description

Homepage: http://sam.johnson.io/research/lsp.html

Paper: http://sam.johnson.io/research/publications/johnson10bmvc.pdf

LSP Dataset Curators

Sam Johnson, Mark Everingham

LSP 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!

LSP Dataset Citation Information

				
					@inproceedings{johnson2010clustered,
  title={Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation.},
  author={Johnson, Sam and Everingham, Mark},
  booktitle={bmvc},
  volume={2},
  number={4},
  pages={5},
  year={2010}
}

LSP Dataset FAQs

What is the LSP dataset for Python?

The Leeds Sports Pose (LSP) is often used as a benchmark for human pose estimation. The dataset is also often used for human body joint detection. The dataset contains 10,000 images gathered from Flickr searches and consists of poses deemed to be challenging to estimate

How to download the LSP dataset in Python?

You can load the LSP 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 LSP dataset training subset and LSP testing subset in Python.

LSP vs LSP -Extended. What is the difference between LSP and LSP -Extended?

The LSP-Extended dataset is similar to the LSP dataset; however, it includes more training samples. The LSP-Extended dataset has 10000 training samples including 1000 training samples from the LSP dataset.