---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-25ep
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: vuongnhathien/30VNFoods
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8486111111111111
---

<!-- 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-25ep

This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the vuongnhathien/30VNFoods dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5506
- Accuracy: 0.8486

## 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: 0.0003
- train_batch_size: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6167        | 1.0   | 275  | 0.5712          | 0.8354   |
| 0.3183        | 2.0   | 550  | 0.5564          | 0.8406   |
| 0.1729        | 3.0   | 825  | 0.5955          | 0.8433   |
| 0.139         | 4.0   | 1100 | 0.6453          | 0.8406   |
| 0.0775        | 5.0   | 1375 | 0.6044          | 0.8517   |
| 0.0784        | 6.0   | 1650 | 0.7265          | 0.8414   |
| 0.0502        | 7.0   | 1925 | 0.6977          | 0.8533   |
| 0.0525        | 8.0   | 2200 | 0.7100          | 0.8549   |
| 0.0311        | 9.0   | 2475 | 0.7423          | 0.8525   |
| 0.026         | 10.0  | 2750 | 0.7901          | 0.8461   |
| 0.0183        | 11.0  | 3025 | 0.7261          | 0.8592   |
| 0.0218        | 12.0  | 3300 | 0.8014          | 0.8485   |
| 0.0135        | 13.0  | 3575 | 0.7391          | 0.8584   |
| 0.0066        | 14.0  | 3850 | 0.6938          | 0.8740   |
| 0.0047        | 15.0  | 4125 | 0.6765          | 0.8815   |
| 0.0052        | 16.0  | 4400 | 0.6611          | 0.8839   |
| 0.0033        | 17.0  | 4675 | 0.6794          | 0.8803   |
| 0.0037        | 18.0  | 4950 | 0.6724          | 0.8811   |
| 0.0026        | 19.0  | 5225 | 0.6759          | 0.8875   |
| 0.0031        | 20.0  | 5500 | 0.6699          | 0.8855   |
| 0.0028        | 21.0  | 5775 | 0.6720          | 0.8847   |
| 0.0029        | 22.0  | 6050 | 0.6746          | 0.8843   |
| 0.0016        | 23.0  | 6325 | 0.6731          | 0.8859   |
| 0.0016        | 24.0  | 6600 | 0.6759          | 0.8859   |
| 0.0019        | 25.0  | 6875 | 0.6767          | 0.8847   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2