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| license: mit |
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| # kernel-brain-data |
| This is a repository to leverage kernel brain data to detect laughter. |
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| The source code is available [on github as kernel-brain-data](https://github.com/efwoods/kernel-brain-data). |
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| ## About the Neural Network Model |
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| This model will take an image of the kernel brain and determine whether the individual is actively laughing. |
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| 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. |
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| The metric results of the model performance are below, and the model is publicly available for download and use. |
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| ## 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 | |
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| | Metric | Value | |
| |--------------|--------| |
| | Accuracy | 0.7819 | |
| | Precision | 0.7143 | |
| | Recall | 0.9163 | |
| | F1-Score | 0.8028 | |
| | ROC AUC | 0.7859 | |
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| ## Model Availability |
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| The model is publicly available and an example notebook of the models use is also available [on the github: kernel-brain-data](https://github.com/efwoods/kernel-brain-data). |
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| ## Data Availability |
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| The training and test data is available on huggingface [here](https://huggingface.co/datasets/evdev3/kernel-neural-data) |
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