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# vit-base-VietnameseFood

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) 
on a Vietnamese Food dataset (https://huggingface.co/datasets/TuyenTrungLe/vietnamese_food_images) with 
More than 17k images were on the train set, 2k5 were on the validation set, and 5k were on the test set.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/658c12791260e506f157abcd/LaCXyFNSgw9PyNaRUK4FK.png)

It achieves the following results on the evaluation set:
- Loss: 1.2489
- Accuracy: 0.8925

Although the loss is quite high, the model predicted well with test set with 0.8639 accuracy and a loss of 0.4871

![image/png](https://cdn-uploads.huggingface.co/production/uploads/658c12791260e506f157abcd/gY9dSYt7hJ_Sf11bTbO6X.png)







## 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: 0.0002
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.4936        | 0.1818 | 100  | 1.5493          | 0.6901   |
| 0.848         | 0.3636 | 200  | 0.9488          | 0.7851   |
| 0.6619        | 0.5455 | 300  | 0.8240          | 0.7865   |
| 0.6868        | 0.7273 | 400  | 0.6671          | 0.8298   |
| 0.6127        | 0.9091 | 500  | 0.6296          | 0.8296   |
| 0.4413        | 1.0909 | 600  | 0.6003          | 0.8339   |
| 0.3484        | 1.2727 | 700  | 0.6349          | 0.8153   |
| 0.3529        | 1.4545 | 800  | 0.5235          | 0.8581   |
| 0.4104        | 1.6364 | 900  | 0.5407          | 0.8512   |
| 0.3097        | 1.8182 | 1000 | 0.5537          | 0.8423   |
| 0.2527        | 2.0    | 1100 | 0.4871          | 0.8639   |
| 0.1571        | 2.1818 | 1200 | 0.5507          | 0.8587   |
| 0.2164        | 2.3636 | 1300 | 0.5598          | 0.8585   |
| 0.1875        | 2.5455 | 1400 | 0.5787          | 0.8522   |
| 0.1314        | 2.7273 | 1500 | 0.5262          | 0.8643   |
| 0.1671        | 2.9091 | 1600 | 0.5686          | 0.8587   |
| 0.0807        | 3.0909 | 1700 | 0.5912          | 0.8633   |
| 0.0989        | 3.2727 | 1800 | 0.6392          | 0.8679   |
| 0.0586        | 3.4545 | 1900 | 0.6587          | 0.8651   |
| 0.0672        | 3.6364 | 2000 | 0.6542          | 0.8758   |
| 0.0342        | 3.8182 | 2100 | 0.6533          | 0.8786   |
| 0.0484        | 4.0    | 2200 | 0.7314          | 0.8756   |
| 0.0678        | 4.1818 | 2300 | 0.8517          | 0.8788   |
| 0.075         | 4.3636 | 2400 | 0.9576          | 0.8843   |
| 0.0201        | 4.5455 | 2500 | 1.0758          | 0.8845   |
| 0.1238        | 4.7273 | 2600 | 1.1375          | 0.8871   |
| 0.0434        | 4.9091 | 2700 | 1.2226          | 0.8877   |
| 0.0493        | 5.0909 | 2800 | 1.1938          | 0.8923   |
| 0.0055        | 5.2727 | 2900 | 1.2594          | 0.8903   |
| 0.0039        | 5.4545 | 3000 | 1.2709          | 0.8887   |
| 0.0445        | 5.6364 | 3100 | 1.2420          | 0.8921   |
| 0.0347        | 5.8182 | 3200 | 1.2609          | 0.8915   |
| 0.0657        | 6.0    | 3300 | 1.2489          | 0.8925   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1

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