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Part 1: Document Description
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Citation |
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Title: |
DeepLabCut Networks for 2D Pose Estimation |
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Identification Number: |
doi:10.7910/DVN/ACNHMP |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2025-01-06 |
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Version: |
1 |
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Bibliographic Citation: |
Özdil, Gizem, 2025, "DeepLabCut Networks for 2D Pose Estimation", https://doi.org/10.7910/DVN/ACNHMP, Harvard Dataverse, V1 |
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Citation |
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Title: |
DeepLabCut Networks for 2D Pose Estimation |
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Identification Number: |
doi:10.7910/DVN/ACNHMP |
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Authoring Entity: |
Özdil, Gizem (EPFL) |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Özdil, Gizem |
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Depositor: |
Özdil, Gizem |
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Date of Deposit: |
2024-12-12 |
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Holdings Information: |
https://doi.org/10.7910/DVN/ACNHMP |
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Study Scope |
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Keywords: |
Medicine, Health and Life Sciences |
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Abstract: |
<p> This folder contains trained DeepLabCut networks for 2D pose estimation from multiple viewpoints, as described in this work. It includes three subfolders, each corresponding to a different network for performing 2D pose estimation on <strong>side</strong> (cam1), <strong>side-front</strong> (cam2), and <strong>front</strong> (cam3) views. We used the same networks for mirror cameras (e.g., the right and left sides). </p> <p> For more details on how to use these networks for 2D pose estimation and how to triangulate them to obtain 3D pose estimation, please visit the <a href="https://github.com/NeLy-EPFL/kinematics3d" target="_blank">NeLy-EPFL/kinematics3d GitHub repository</a>. </p> <p> The accompanying PDF (tracked_keypoints_setup.pdf) provides information about the locations of the tracked points. </p> <p> To uncompress the file, run the following command in the terminal: </p> <pre> $ tar -xvf filename.tgz </pre> |
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Methodology and Processing |
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Sources Statement |
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Data Access |
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Other Study Description Materials |
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Label: |
cam1.tgz |
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Notes: |
application/x-gzip |
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cam2.tgz |
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Notes: |
application/x-gzip |
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cam3.tgz |
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Notes: |
application/x-gzip |
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Label: |
tracked_keypoints_setup.pdf |
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Notes: |
application/pdf |