---
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-org-plot
  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: 0.885515873015873
---

<!-- 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-org-plot

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: 0.5308
- Accuracy: 0.8855

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5888        | 1.0   | 275  | 0.6249          | 0.8179   |
| 0.2832        | 2.0   | 550  | 0.5429          | 0.8537   |
| 0.1483        | 3.0   | 825  | 0.5962          | 0.8453   |
| 0.0884        | 4.0   | 1100 | 0.5802          | 0.8573   |
| 0.034         | 5.0   | 1375 | 0.5869          | 0.8688   |
| 0.0214        | 6.0   | 1650 | 0.5424          | 0.8823   |
| 0.0088        | 7.0   | 1925 | 0.5372          | 0.8903   |
| 0.006         | 8.0   | 2200 | 0.5404          | 0.8871   |
| 0.0021        | 9.0   | 2475 | 0.5240          | 0.8915   |
| 0.0033        | 10.0  | 2750 | 0.5256          | 0.8930   |


### Framework versions

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