metadata
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
vit-driver-drowsiness-detection
This model is a fine-tuned version of 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