# Google - Isolated Sign Language Recognition — Data

来源：https://www.kaggle.com/competitions/asl-signs/data

本地 Meta Kaggle 快照；不是本次实时页面。

# Dataset Description

Deaf children are often born to hearing parents who do not know sign language. Your challenge in this competition is to help identify signs made in processed videos, which will support the development of mobile apps to help teach parents sign language so they can communicate with their Deaf children. 


This competition requires submissions to be made in the form of [TensorFlow Lite models](https://www.tensorflow.org/lite). You are welcome to train your model using the framework of your choice as long as you convert the model checkpoint into the tflite format prior to submission. Please see [the evaluation page](https://www.kaggle.com/competitions/asl-signs/overview/evaluation) for details.

Update: the rerun dataset [has been published here](https://www.kaggle.com/datasets/sohier/461054610546105/).

##Files

**train_landmark_files/[participant_id]/[sequence_id].parquet** The landmark data. The landmarks were extracted from raw videos with the [MediaPipe holistic model](https://google.github.io/mediapipe/solutions/holistic.html). Not all of the frames necessarily had visible hands or hands that could be detected by the model.

_Landmark data should not be used to identify or re-identify an individual. Landmark data is not intended to enable any form of identity recognition or store any unique biometric identification._

*   `frame` - The frame number in the raw video.
*   `row_id` -  A unique identifier for the row.
*   `type` - The type of landmark. One of `['face', 'left_hand', 'pose', 'right_hand']`.
*   `landmark_index` - The landmark index number. Details of the [hand landmark locations can be found here](https://google.github.io/mediapipe/solutions/hands.html#hand-landmark-model).
*   `[x/y/z]` - The normalized spatial coordinates of the landmark. These are the only columns that will be provided to your submitted model for inference. The MediaPipe model is not fully trained to predict depth so you may wish to ignore the z values. 


**train.csv** 

*   `path` - The path to the landmark file.
*   `participant_id` - A unique identifier for the data contributor.
*   `sequence_id` - A unique identifier for the landmark sequence.
*   `sign` - The label for the landmark sequence.