---
license: apache-2.0
base_model: facebook/deit-base-patch16-224
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: deit-base
  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: 0.8398809523809524
---

<!-- 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. -->

# deit-base

This model is a fine-tuned version of [facebook/deit-base-patch16-224](https://huggingface.co/facebook/deit-base-patch16-224) on the vuongnhathien/30VNFoods dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7340
- Accuracy: 0.8399

## 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: cosine
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9389        | 1.0   | 275  | 0.9157          | 0.7268   |
| 0.5942        | 2.0   | 550  | 0.8308          | 0.7622   |
| 0.3701        | 3.0   | 825  | 0.7638          | 0.7873   |
| 0.2164        | 4.0   | 1100 | 0.8891          | 0.7825   |
| 0.1215        | 5.0   | 1375 | 0.8665          | 0.7932   |
| 0.0563        | 6.0   | 1650 | 0.8048          | 0.8215   |
| 0.0169        | 7.0   | 1925 | 0.7529          | 0.8370   |
| 0.0068        | 8.0   | 2200 | 0.7177          | 0.8505   |
| 0.0032        | 9.0   | 2475 | 0.6994          | 0.8533   |
| 0.0036        | 10.0  | 2750 | 0.6989          | 0.8529   |


### Framework versions

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