---
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-5e-4
  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: 0.8682539682539683
---

<!-- 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-5e-4

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: 0.6221
- Accuracy: 0.8683

## 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: 0.0005
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7552        | 1.0   | 275  | 0.7489          | 0.7849   |
| 0.4161        | 2.0   | 550  | 0.6816          | 0.8127   |
| 0.2389        | 3.0   | 825  | 0.6486          | 0.8326   |
| 0.1523        | 4.0   | 1100 | 0.6459          | 0.8414   |
| 0.0917        | 5.0   | 1375 | 0.7039          | 0.8382   |
| 0.0492        | 6.0   | 1650 | 0.7023          | 0.8425   |
| 0.0175        | 7.0   | 1925 | 0.6089          | 0.8664   |
| 0.009         | 8.0   | 2200 | 0.5864          | 0.8775   |
| 0.0026        | 9.0   | 2475 | 0.5646          | 0.8783   |
| 0.0037        | 10.0  | 2750 | 0.5681          | 0.8803   |


### Framework versions

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