The model described in this card is a classification network, which aims to classify human emotion into 6 categories.
This model is based on five fully connected layers.
The training algorithm optimizes the network to minimize the root mean square error between predicted and ground truth point of regards. This model was trained using the Emotion Classification training app in TAO Toolkit v3.0.
Intended use case¶
Primary use case for this model is to detect human emotion. The model can be used to detect human emotion from photos and videos by using appropriate video or image decoding and pre-processing. The model takes in facial landmarks as input and provide emotion classes as output.
The datasheet for the model is captured in its model card hosted at NGC.