VIT_Drowsiness_2

VIT_Drowsiness_2

This model is a fine-tuned version of 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]