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evdev3/
kernel-neural-image-laughter-classifier

mit

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kernel-brain-data

This is a repository to leverage kernel brain data to detect laughter.

The source code is available on github as kernel-brain-data.

About the Neural Network Model

This model will take an image of the kernel brain and determine whether the individual is actively laughing.

The Kernel Neural Image model Convolutional Neural Network alone achieves accurate results on predicting laughter vs. non-laughter when an input image of the live kernel brain is used as input to the network. The model uses pre-trained weights from resnet-18 as well as frames from the Lex Fridman podcast.

The metric results of the model performance are below, and the model is publicly available for download and use.

Metrics

Class	Precision	Recall	F1-score	Support
Non-Laughter	0.89	0.66	0.76	267
Laughter	0.71	0.92	0.80	251
Accuracy			0.78	518
Macro Avg	0.80	0.79	0.78	518
Weighted Avg	0.81	0.78	0.78	518

Metric	Value
Accuracy	0.7819
Precision	0.7143
Recall	0.9163
F1-Score	0.8028
ROC AUC	0.7859

Model Availability

The model is publicly available and an example notebook of the models use is also available on the github: kernel-brain-data.

Data Availability

The training and test data is available on huggingface here

evdev3/kernel-neural-image-laughter-classifier