---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-Diastarallclasses
  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. -->

# vit-base-patch16-224-Diastarallclasses

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: 0.0345
- Accuracy: 0.9811

## 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: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2142        | 1.0   | 459  | 0.0876          | 0.9643   |
| 0.1826        | 2.0   | 918  | 0.0658          | 0.9685   |
| 0.1469        | 3.0   | 1377 | 0.0527          | 0.9721   |
| 0.1637        | 4.0   | 1836 | 0.0463          | 0.9737   |
| 0.111         | 5.0   | 2295 | 0.0476          | 0.9748   |
| 0.1467        | 6.0   | 2754 | 0.0393          | 0.9777   |
| 0.1284        | 7.0   | 3213 | 0.0382          | 0.9787   |
| 0.1025        | 8.0   | 3672 | 0.0396          | 0.9777   |
| 0.1301        | 9.0   | 4131 | 0.0378          | 0.9782   |
| 0.0829        | 10.0  | 4590 | 0.0345          | 0.9811   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1