
<!-- 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
{"license": "apache-2.0", "library_name": "transformers", "tags": ["image-classification", "generated_from_trainer"], "datasets": ["uta_rldd"], "metrics": ["accuracy"], "model-index": [{"name": "vit-base-driver-drowsiness-detection", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "chbh7051/vit-base-driver-drowsiness-detection", "type": "uta_rldd", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9751972942502819, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard vit image-classification generated_from_trainer dataset:uta_rldd license:apache-2.0 model-index autotrain_compatible endpoints_compatible 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 #region-us 

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



### 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: 6
* 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 #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: 6
* 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 #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: 6
* 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