---
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-30VN
  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: 0.8920634920634921
---

<!-- 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-30VN

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: 0.5335
- Accuracy: 0.8921

## 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6059        | 1.0   | 275  | 0.5290          | 0.8425   |
| 0.284         | 2.0   | 550  | 0.5239          | 0.8569   |
| 0.1336        | 3.0   | 825  | 0.6038          | 0.8469   |
| 0.0807        | 4.0   | 1100 | 0.5934          | 0.8628   |
| 0.0357        | 5.0   | 1375 | 0.6220          | 0.8588   |
| 0.0206        | 6.0   | 1650 | 0.5674          | 0.8803   |
| 0.0105        | 7.0   | 1925 | 0.5276          | 0.8907   |
| 0.005         | 8.0   | 2200 | 0.5096          | 0.8922   |
| 0.0018        | 9.0   | 2475 | 0.5064          | 0.8926   |
| 0.0035        | 10.0  | 2750 | 0.5055          | 0.8974   |


### Framework versions

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