---
language: en
license: mit
tags:
- VIT
- image-classification
- drowsiness-detection
---

# VIT_Drowsiness_2

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) for drowsiness detection.

## Model description

This model is a Vision Transformer (ViT) fine-tuned for drowsiness detection. It classifies images into two categories: drowsy and not drowsy.

## Intended uses & limitations

This model is intended for drowsiness detection in images. It should be used on facial images similar to those in the training dataset.

## Training data

The model was trained on a custom dataset located at /kaggle/input/nthuddd2/train_data. The dataset was split into 70% training, 15% validation, and 15% test sets.

## Training procedure

The model was trained for 10 epochs using the Lion optimizer with a learning rate of 0.0001 and weight decay of 0.01. A cosine learning rate scheduler with 0.1 warmup ratio was used.

## Evaluation results

[Add your evaluation results here after training]