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FLIC
2D Keypoints
Pose Estimation
|...
License: MIT

Overview

We collected a 5003 image dataset automatically from popular Hollywood movies.
The images were obtained by running a state-of-the-art person detector on every tenth frame
of 30 movies. People detected with high confidence (roughly 20K candidates) were then sent
to the crowdsourcing marketplace Amazon Mechanical Turk to obtain groundtruthlabeling.
Each image was annotated by five Turkers for $0.01 each to label 10 upperbody joints.
The median-of-five labeling was taken in each image to be robust to outlier annotation.
Finally, images were rejected manually by us if the person was occluded or severely non-frontal.
We set aside 20% (1016 images) of the data for testing.

Data Format

File Size Description
FLIC.zip 287MB 5003 examples used in our CVPR13 MODEC paper.
FLIC-full.zip 1.2GB 20928 examples, a superset of FLIC consisting of more difficult examples (see below). NOTE: please do not use this as training data if testing on the FLIC test set. It is a superset of the original FLIC dataset and will lead to overfitting. Choose a sensible split where no two frames from the same movie shot cross the train/test divide.

Citation

Please use the following citation when referencing the dataset:

  @inproceedings{modec13,
    title={MODEC: Multimodal Decomposable Models for Human Pose Estimation},
    author={Sapp, Benjamin and Taskar, Ben},
    booktitle={In Proc. CVPR},
    year={2013},
  }

License

MIT

Data Summary
Type
Image,
Amount
--
Size
1.38GB
Provided by
GRASP Laboratory
The General Robotics, Automation, Sensing and Perception (GRASP) Lab is a multidisciplinary research laboratory at the University of Pennsylvania.
| Amount -- | Size 1.38GB
FLIC
2D Keypoints
Pose Estimation
License: MIT

Overview

We collected a 5003 image dataset automatically from popular Hollywood movies.
The images were obtained by running a state-of-the-art person detector on every tenth frame
of 30 movies. People detected with high confidence (roughly 20K candidates) were then sent
to the crowdsourcing marketplace Amazon Mechanical Turk to obtain groundtruthlabeling.
Each image was annotated by five Turkers for $0.01 each to label 10 upperbody joints.
The median-of-five labeling was taken in each image to be robust to outlier annotation.
Finally, images were rejected manually by us if the person was occluded or severely non-frontal.
We set aside 20% (1016 images) of the data for testing.

Data Format

File Size Description
FLIC.zip 287MB 5003 examples used in our CVPR13 MODEC paper.
FLIC-full.zip 1.2GB 20928 examples, a superset of FLIC consisting of more difficult examples (see below). NOTE: please do not use this as training data if testing on the FLIC test set. It is a superset of the original FLIC dataset and will lead to overfitting. Choose a sensible split where no two frames from the same movie shot cross the train/test divide.

Citation

Please use the following citation when referencing the dataset:

  @inproceedings{modec13,
    title={MODEC: Multimodal Decomposable Models for Human Pose Estimation},
    author={Sapp, Benjamin and Taskar, Ben},
    booktitle={In Proc. CVPR},
    year={2013},
  }

License

MIT

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