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---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
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: 0.5555
- Accuracy: 0.7471
- Roc Auc: 0.7154
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------:|
| 0.5439 | 0.14 | 50 | 0.5544 | 0.7429 | 0.6942 |
| 0.5448 | 0.29 | 100 | 0.6509 | 0.74 | 0.6629 |
| 0.6165 | 0.43 | 150 | 0.5599 | 0.7129 | 0.7074 |
| 0.5916 | 0.57 | 200 | 0.5959 | 0.6829 | 0.7009 |
| 0.5285 | 0.71 | 250 | 0.5801 | 0.73 | 0.6975 |
| 0.4861 | 0.86 | 300 | 0.5866 | 0.7486 | 0.6621 |
| 0.5428 | 1.0 | 350 | 0.5477 | 0.7443 | 0.6801 |
| 0.5535 | 1.14 | 400 | 0.5360 | 0.7529 | 0.7033 |
| 0.441 | 1.29 | 450 | 0.5850 | 0.7571 | 0.7078 |
| 0.6003 | 1.43 | 500 | 0.5268 | 0.7586 | 0.7243 |
| 0.4686 | 1.57 | 550 | 0.5223 | 0.7571 | 0.7306 |
| 0.5477 | 1.71 | 600 | 0.5753 | 0.7529 | 0.7188 |
| 0.5633 | 1.86 | 650 | 0.5456 | 0.74 | 0.7246 |
| 0.4799 | 2.0 | 700 | 0.5442 | 0.7386 | 0.7018 |
| 0.5373 | 2.14 | 750 | 0.6535 | 0.6443 | 0.6950 |
| 0.4244 | 2.29 | 800 | 0.5304 | 0.7514 | 0.7145 |
| 0.4984 | 2.43 | 850 | 0.5739 | 0.7043 | 0.6936 |
| 0.5012 | 2.57 | 900 | 0.5405 | 0.7514 | 0.7102 |
| 0.4852 | 2.71 | 950 | 0.5314 | 0.7471 | 0.7290 |
| 0.5498 | 2.86 | 1000 | 0.5490 | 0.7429 | 0.7094 |
| 0.4547 | 3.0 | 1050 | 0.6028 | 0.7443 | 0.7264 |
| 0.5145 | 3.14 | 1100 | 0.5699 | 0.7214 | 0.7028 |
| 0.475 | 3.29 | 1150 | 0.5493 | 0.7457 | 0.7052 |
| 0.4632 | 3.43 | 1200 | 0.5570 | 0.7414 | 0.7018 |
| 0.408 | 3.57 | 1250 | 0.5744 | 0.7514 | 0.6993 |
| 0.3851 | 3.71 | 1300 | 0.5600 | 0.73 | 0.7102 |
| 0.4093 | 3.86 | 1350 | 0.5587 | 0.7557 | 0.7143 |
| 0.4628 | 4.0 | 1400 | 0.5555 | 0.7471 | 0.7154 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
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