Gesture Recognition
===================

Model Overview
--------------

The model described in this card is a classification network that aims to classify hand-crop images
into five gesture types:

* thumbs up
* fist
* stop
* ok
* two
* random

GestureNet is generally cascaded with Bodypose for gesture-based applications.

Model Architecture
------------------

This is a classification model with a Resnet18 backbone.


Training Algorithm
------------------

The training algorithm optimizes the network to minimize the categorical cross entropy loss for the
classes.
This model was trained using the :ref:`Gesture Recognition<gesturenet>` training app in TLT v3.0.

Reference
---------

* He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In
  Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

Intended Use
------------

GestureNet is cascaded with a hand detect or a bodypose network. For example, BodyPoseNet
detects human body joints, which are used to create hand crops, and GestureNet acts as a classifier
that determines the gesture of the hand.

**Input**

- RGB Images of 160 X 160 X 3

**Output**

- Gesture category labels

The datasheet for the model is captured in its model card hosted on `NGC`_.

.. _NGC: https://ngc.nvidia.com/catalog/models/nvidia:tlt_gesturenet