---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-ori-dataset-exp
  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-ori-dataset-exp

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: 0.6257
- Accuracy: 0.8506

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6531        | 0.31  | 100  | 0.6383          | 0.7718   |
| 0.6366        | 0.62  | 200  | 0.8169          | 0.7302   |
| 0.7064        | 0.93  | 300  | 0.6012          | 0.7840   |
| 0.4821        | 1.25  | 400  | 0.8299          | 0.7063   |
| 0.474         | 1.56  | 500  | 0.6822          | 0.7885   |
| 0.3619        | 1.87  | 600  | 0.5275          | 0.8076   |
| 0.1723        | 2.18  | 700  | 0.6328          | 0.7868   |
| 0.2579        | 2.49  | 800  | 0.5694          | 0.8121   |
| 0.1422        | 2.8   | 900  | 0.6486          | 0.8245   |
| 0.0528        | 3.12  | 1000 | 0.5941          | 0.8398   |
| 0.0203        | 3.43  | 1100 | 0.6370          | 0.8502   |
| 0.011         | 3.74  | 1200 | 0.6257          | 0.8506   |


### Framework versions

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