---
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-add-2-decay
  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-add-2-decay

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: 0.5253
- 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: 0.0003
- 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: cosine
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6211        | 1.0   | 275  | 0.6582          | 0.7996   |
| 0.3091        | 2.0   | 550  | 0.5436          | 0.8457   |
| 0.1674        | 3.0   | 825  | 0.5812          | 0.8565   |
| 0.0947        | 4.0   | 1100 | 0.5674          | 0.8648   |
| 0.0335        | 5.0   | 1375 | 0.6408          | 0.8517   |
| 0.0235        | 6.0   | 1650 | 0.5589          | 0.8803   |
| 0.006         | 7.0   | 1925 | 0.5129          | 0.8859   |
| 0.0054        | 8.0   | 2200 | 0.4975          | 0.8922   |
| 0.0017        | 9.0   | 2475 | 0.4996          | 0.8926   |
| 0.0027        | 10.0  | 2750 | 0.4998          | 0.8915   |


### Framework versions

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