2 datasets found
  1. a

    MPII Human Pose Dataset

    • academictorrents.com
    • opendatalab.com
    bittorrent
    Updated Jun 13, 2020
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    None (2020). MPII Human Pose Dataset [Dataset]. https://academictorrents.com/details/6be335f0d038fd4ed4422dd318705e0843059718
    Explore at:
    bittorrent(12101283689)Available download formats
    Dataset updated
    Jun 13, 2020
    Authors
    None
    License

    https://academictorrents.com/nolicensespecifiedhttps://academictorrents.com/nolicensespecified

    Description

    MPII Human Pose dataset is a state of the art benchmark for evaluation of articulated human pose estimation. The dataset includes around 25K images containing over 40K people with annotated body joints. The images were systematically collected using an established taxonomy of every day human activities. Overall the dataset covers 410 human activities and each image is provided with an activity label. Each image was extracted from a YouTube video and provided with preceding and following un-annotated frames. In addition, for the test set we obtained richer annotations including body part occlusions and 3D torso and head orientations. Following the best practices for the performance evaluation benchmarks in the literature we withhold the test annotations to prevent overfitting and tuning on the test set. We are working on an automatic evaluation server and performance analysis tools based on rich test set annotations. Citing the dataset

  2. h

    MPII_Human_Pose_Dataset

    • huggingface.co
    Updated May 7, 2024
    Share
    FacebookFacebook
    TwitterTwitter
    Email
    Click to copy link
    Link copied
    Close
    Cite
    Voxel51 (2024). MPII_Human_Pose_Dataset [Dataset]. https://huggingface.co/datasets/Voxel51/MPII_Human_Pose_Dataset
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    May 7, 2024
    Dataset authored and provided by
    Voxel51
    License

    https://choosealicense.com/licenses/bsd-2-clause/https://choosealicense.com/licenses/bsd-2-clause/

    Description

    Dataset Card for MPII Human Pose

    MPII Human Pose dataset is a state of the art benchmark for evaluation of articulated human pose estimation. The dataset includes around 25K images containing over 40K people with annotated body joints. The images were systematically collected using an established taxonomy of every day human activities. Overall the dataset covers 410 human activities and each image is provided with an activity label. Each image was extracted from a YouTube video… See the full description on the dataset page: https://huggingface.co/datasets/Voxel51/MPII_Human_Pose_Dataset.

  3. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
None (2020). MPII Human Pose Dataset [Dataset]. https://academictorrents.com/details/6be335f0d038fd4ed4422dd318705e0843059718

MPII Human Pose Dataset

Explore at:
bittorrent(12101283689)Available download formats
Dataset updated
Jun 13, 2020
Authors
None
License

https://academictorrents.com/nolicensespecifiedhttps://academictorrents.com/nolicensespecified

Description

MPII Human Pose dataset is a state of the art benchmark for evaluation of articulated human pose estimation. The dataset includes around 25K images containing over 40K people with annotated body joints. The images were systematically collected using an established taxonomy of every day human activities. Overall the dataset covers 410 human activities and each image is provided with an activity label. Each image was extracted from a YouTube video and provided with preceding and following un-annotated frames. In addition, for the test set we obtained richer annotations including body part occlusions and 3D torso and head orientations. Following the best practices for the performance evaluation benchmarks in the literature we withhold the test annotations to prevent overfitting and tuning on the test set. We are working on an automatic evaluation server and performance analysis tools based on rich test set annotations. Citing the dataset

Search
Clear search
Close search
Google apps
Main menu