Update README.md

README.md

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-license: mit
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+---
+license: mit
+---
+# 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](https://github.com/efwoods/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](https://github.com/efwoods/kernel-brain-data).
+
+## Data Availability
+
+The training and test data is available on huggingface [here](https://huggingface.co/datasets/evdev3/kernel-neural-data)