---
license: apache-2.0
tags:
- generated_from_trainer
base_model: google/vit-base-patch16-224
metrics:
- accuracy
model-index:
- name: model
  results: []
---

<!-- 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. -->

# model

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5652
- Accuracy: 0.7486
- Roc Auc: 0.7023

## 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: 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: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|
| 0.5665        | 0.14  | 50   | 0.5829          | 0.71     | 0.6554  |
| 0.5428        | 0.29  | 100  | 0.6787          | 0.71     | 0.6873  |
| 0.5793        | 0.43  | 150  | 0.5501          | 0.7429   | 0.6910  |
| 0.567         | 0.57  | 200  | 0.5489          | 0.7443   | 0.6951  |
| 0.5427        | 0.71  | 250  | 0.5758          | 0.73     | 0.6809  |
| 0.5022        | 0.86  | 300  | 0.5784          | 0.7229   | 0.6489  |
| 0.5415        | 1.0   | 350  | 0.5530          | 0.7429   | 0.6791  |
| 0.5731        | 1.14  | 400  | 0.5440          | 0.7457   | 0.6955  |
| 0.4746        | 1.29  | 450  | 0.5632          | 0.7486   | 0.6916  |
| 0.6076        | 1.43  | 500  | 0.5356          | 0.7571   | 0.7089  |
| 0.4674        | 1.57  | 550  | 0.5477          | 0.7471   | 0.7247  |
| 0.546         | 1.71  | 600  | 0.5774          | 0.7457   | 0.7038  |
| 0.5776        | 1.86  | 650  | 0.5367          | 0.7443   | 0.7139  |
| 0.4802        | 2.0   | 700  | 0.5418          | 0.7429   | 0.7038  |
| 0.5612        | 2.14  | 750  | 0.6319          | 0.6714   | 0.6911  |
| 0.4281        | 2.29  | 800  | 0.5550          | 0.7443   | 0.6951  |
| 0.518         | 2.43  | 850  | 0.6038          | 0.7014   | 0.6743  |
| 0.505         | 2.57  | 900  | 0.5480          | 0.7486   | 0.7036  |
| 0.4689        | 2.71  | 950  | 0.5304          | 0.7571   | 0.7191  |
| 0.5685        | 2.86  | 1000 | 0.5453          | 0.7557   | 0.7009  |
| 0.4624        | 3.0   | 1050 | 0.6102          | 0.7386   | 0.7176  |
| 0.5246        | 3.14  | 1100 | 0.5674          | 0.7243   | 0.6932  |
| 0.4601        | 3.29  | 1150 | 0.5538          | 0.74     | 0.7035  |
| 0.4663        | 3.43  | 1200 | 0.5531          | 0.75     | 0.7036  |
| 0.4084        | 3.57  | 1250 | 0.5787          | 0.7429   | 0.6901  |
| 0.3992        | 3.71  | 1300 | 0.5691          | 0.7386   | 0.6965  |
| 0.4385        | 3.86  | 1350 | 0.5701          | 0.7457   | 0.7012  |
| 0.5024        | 4.0   | 1400 | 0.5652          | 0.7486   | 0.7023  |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2