---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- uta_rldd
metrics:
- accuracy
model-index:
- name: vit-driver-drowsiness-detection
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: chbh7051/driver-drowsiness-detection
      type: uta_rldd
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9930477264186396
---

<!-- 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-driver-drowsiness-detection

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the chbh7051/driver-drowsiness-detection dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0159
- Accuracy: 0.9930

## 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: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1504        | 0.17  | 500  | 0.1178          | 0.9540   |
| 0.0581        | 0.33  | 1000 | 0.1022          | 0.9579   |
| 0.0415        | 0.5   | 1500 | 0.0877          | 0.9746   |
| 0.0487        | 0.67  | 2000 | 0.0650          | 0.9775   |
| 0.0555        | 0.84  | 2500 | 0.0537          | 0.9786   |
| 0.0279        | 1.0   | 3000 | 0.0472          | 0.9827   |
| 0.0139        | 1.17  | 3500 | 0.0452          | 0.9855   |
| 0.0282        | 1.34  | 4000 | 0.0358          | 0.9878   |
| 0.0077        | 1.5   | 4500 | 0.0397          | 0.9876   |
| 0.0143        | 1.67  | 5000 | 0.0159          | 0.9930   |
| 0.0439        | 1.84  | 5500 | 0.0162          | 0.9930   |


### Framework versions

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