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# 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

{"license": "apache-2.0", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["uta_rldd"], "metrics": ["accuracy"], "model-index": [{"name": "vit-driver-drowsiness-detection", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "chbh7051/driver-drowsiness-detection", "type": "uta_rldd", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9930477264186396, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer dataset:uta_rldd license:apache-2.0 model-index autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-uta_rldd #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 

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



### Framework versions


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



### 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 ### Framework versions


* Transformers 4.27.4
* Pytorch 1.13.0
* Datasets 2.1.0
* Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #generated_from_trainer #dataset-uta_rldd #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### 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 ### Framework versions


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