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# vit-base-patch16-224-for-pre_evaluation

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6048
- Accuracy: 0.3929

## 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: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5774        | 0.98  | 16   | 1.5109          | 0.3022   |
| 1.4794        | 1.97  | 32   | 1.4942          | 0.3242   |
| 1.4536        | 2.95  | 48   | 1.4943          | 0.3187   |
| 1.421         | 4.0   | 65   | 1.4247          | 0.3407   |
| 1.3882        | 4.98  | 81   | 1.4944          | 0.3462   |
| 1.3579        | 5.97  | 97   | 1.4180          | 0.3571   |
| 1.2838        | 6.95  | 113  | 1.4693          | 0.3681   |
| 1.2695        | 8.0   | 130  | 1.4359          | 0.3434   |
| 1.2016        | 8.98  | 146  | 1.4656          | 0.3599   |
| 1.2087        | 9.97  | 162  | 1.4550          | 0.3379   |
| 1.206         | 10.95 | 178  | 1.5056          | 0.3516   |
| 1.1236        | 12.0  | 195  | 1.5003          | 0.3434   |
| 1.0534        | 12.98 | 211  | 1.5193          | 0.3269   |
| 1.0024        | 13.97 | 227  | 1.4890          | 0.3681   |
| 0.9767        | 14.95 | 243  | 1.5628          | 0.3434   |
| 0.9201        | 16.0  | 260  | 1.6306          | 0.3516   |
| 0.9136        | 16.98 | 276  | 1.5715          | 0.3626   |
| 0.8566        | 17.97 | 292  | 1.5966          | 0.3654   |
| 0.8273        | 18.95 | 308  | 1.6048          | 0.3929   |
| 0.7825        | 20.0  | 325  | 1.6175          | 0.3846   |
| 0.736         | 20.98 | 341  | 1.6526          | 0.3929   |
| 0.7008        | 21.97 | 357  | 1.6563          | 0.3736   |
| 0.6714        | 22.95 | 373  | 1.7319          | 0.3901   |
| 0.7039        | 24.0  | 390  | 1.6866          | 0.3929   |
| 0.628         | 24.98 | 406  | 1.7023          | 0.3791   |
| 0.6182        | 25.97 | 422  | 1.7301          | 0.3901   |
| 0.5957        | 26.95 | 438  | 1.7157          | 0.3846   |
| 0.5973        | 28.0  | 455  | 1.7478          | 0.3709   |
| 0.5655        | 28.98 | 471  | 1.7377          | 0.3736   |
| 0.5631        | 29.54 | 480  | 1.7374          | 0.3736   |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3

0_soft 1_semi_medium 2_medium 3_semi_hard 4_hard