metadata

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

vit-base-driver-drowsiness-detection

This model is a fine-tuned version of 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