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# vit-base-patch16-224-finetuned-teeth_dataset

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1736
- Accuracy: 0.9348

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 0.8   | 3    | 4.6533          | 0.0087   |
| No log        | 1.87  | 7    | 4.5848          | 0.0065   |
| 4.6048        | 2.93  | 11   | 4.4608          | 0.0304   |
| 4.6048        | 4.0   | 15   | 4.2857          | 0.0848   |
| 4.6048        | 4.8   | 18   | 4.1470          | 0.1152   |
| 4.2716        | 5.87  | 22   | 3.9641          | 0.2043   |
| 4.2716        | 6.93  | 26   | 3.7705          | 0.3152   |
| 3.7404        | 8.0   | 30   | 3.5809          | 0.4196   |
| 3.7404        | 8.8   | 33   | 3.4766          | 0.4522   |
| 3.7404        | 9.87  | 37   | 3.2981          | 0.5087   |
| 3.1589        | 10.93 | 41   | 3.1132          | 0.6087   |
| 3.1589        | 12.0  | 45   | 2.9494          | 0.6696   |
| 3.1589        | 12.8  | 48   | 2.8361          | 0.6783   |
| 2.6384        | 13.87 | 52   | 2.6521          | 0.7348   |
| 2.6384        | 14.93 | 56   | 2.4943          | 0.7587   |
| 2.1342        | 16.0  | 60   | 2.3422          | 0.7848   |
| 2.1342        | 16.8  | 63   | 2.2327          | 0.8109   |
| 2.1342        | 17.87 | 67   | 2.0834          | 0.8261   |
| 1.714         | 18.93 | 71   | 1.9834          | 0.8565   |
| 1.714         | 20.0  | 75   | 1.8932          | 0.8674   |
| 1.714         | 20.8  | 78   | 1.8618          | 0.8587   |
| 1.4427        | 21.87 | 82   | 1.6974          | 0.8891   |
| 1.4427        | 22.93 | 86   | 1.6663          | 0.8891   |
| 1.1858        | 24.0  | 90   | 1.6014          | 0.8848   |
| 1.1858        | 24.8  | 93   | 1.5112          | 0.9043   |
| 1.1858        | 25.87 | 97   | 1.4732          | 0.9109   |
| 1.0222        | 26.93 | 101  | 1.4304          | 0.9065   |
| 1.0222        | 28.0  | 105  | 1.3915          | 0.9130   |
| 1.0222        | 28.8  | 108  | 1.3509          | 0.9217   |
| 0.8306        | 29.87 | 112  | 1.3054          | 0.9283   |
| 0.8306        | 30.93 | 116  | 1.2870          | 0.9261   |
| 0.7391        | 32.0  | 120  | 1.2645          | 0.9283   |
| 0.7391        | 32.8  | 123  | 1.2454          | 0.9261   |
| 0.7391        | 33.87 | 127  | 1.2395          | 0.9283   |
| 0.6971        | 34.93 | 131  | 1.2076          | 0.9304   |
| 0.6971        | 36.0  | 135  | 1.1821          | 0.9326   |
| 0.6971        | 36.8  | 138  | 1.1736          | 0.9348   |
| 0.6758        | 37.87 | 142  | 1.1671          | 0.9326   |
| 0.6758        | 38.93 | 146  | 1.1656          | 0.9348   |
| 0.6445        | 40.0  | 150  | 1.1649          | 0.9348   |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2

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