---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- uta_rldd
metrics:
- accuracy
model-index:
- name: vit-base-driver-drowsiness-detection
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: chbh7051/vit-base-driver-drowsiness-detection
      type: uta_rldd
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9751972942502819
library_name: transformers
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# vit-base-driver-drowsiness-detection

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the chbh7051/driver-drowsiness-detection dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0800
- Accuracy: 0.9752

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.4811        | 0.6   | 2000  | 0.5214          | 0.7636   |
| 0.3339        | 1.2   | 4000  | 0.3437          | 0.8621   |
| 0.284         | 1.8   | 6000  | 0.2679          | 0.8932   |
| 0.2143        | 2.41  | 8000  | 0.2269          | 0.9125   |
| 0.0997        | 3.01  | 10000 | 0.1576          | 0.9444   |
| 0.1168        | 3.61  | 12000 | 0.1214          | 0.9596   |
| 0.0873        | 4.21  | 14000 | 0.1256          | 0.9550   |
| 0.06          | 4.81  | 16000 | 0.0800          | 0.9752   |


### Framework versions

- Transformers 4.27.4
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2