---
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-1e-4-15ep
  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: 0.8867063492063492
---

<!-- 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-1e-4-15ep

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: 0.3897
- Accuracy: 0.8867

## 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: 0.0001
- 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: cosine
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5399        | 1.0   | 275  | 0.4756          | 0.8676   |
| 0.2126        | 2.0   | 550  | 0.4134          | 0.8875   |
| 0.0726        | 3.0   | 825  | 0.4687          | 0.8775   |
| 0.0345        | 4.0   | 1100 | 0.4552          | 0.8883   |
| 0.0123        | 5.0   | 1375 | 0.5129          | 0.8851   |
| 0.0068        | 6.0   | 1650 | 0.4877          | 0.8954   |
| 0.0063        | 7.0   | 1925 | 0.4667          | 0.9018   |
| 0.0055        | 8.0   | 2200 | 0.4697          | 0.9030   |
| 0.0021        | 9.0   | 2475 | 0.4620          | 0.9054   |
| 0.0039        | 10.0  | 2750 | 0.4652          | 0.9058   |
| 0.0027        | 11.0  | 3025 | 0.4658          | 0.9058   |
| 0.0024        | 12.0  | 3300 | 0.4668          | 0.9078   |
| 0.0021        | 13.0  | 3575 | 0.4671          | 0.9078   |
| 0.0019        | 14.0  | 3850 | 0.4681          | 0.9062   |
| 0.002         | 15.0  | 4125 | 0.4682          | 0.9062   |


### Framework versions

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