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# finetuned-indian-food

This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6866
- Accuracy: 0.8286

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 2.9933        | 0.1946 | 100  | 3.0302          | 0.4679   |
| 2.4249        | 0.3891 | 200  | 2.2643          | 0.5594   |
| 2.1319        | 0.5837 | 300  | 2.0092          | 0.5691   |
| 1.7793        | 0.7782 | 400  | 1.7550          | 0.6086   |
| 1.8401        | 0.9728 | 500  | 1.6372          | 0.6407   |
| 1.3499        | 1.1673 | 600  | 1.3922          | 0.6908   |
| 1.2906        | 1.3619 | 700  | 1.2705          | 0.7118   |
| 0.9804        | 1.5564 | 800  | 1.1602          | 0.7322   |
| 1.0783        | 1.7510 | 900  | 1.1229          | 0.7308   |
| 0.968         | 1.9455 | 1000 | 1.0598          | 0.7468   |
| 0.6267        | 2.1401 | 1100 | 0.9519          | 0.7663   |
| 0.7739        | 2.3346 | 1200 | 0.9060          | 0.7785   |
| 0.8705        | 2.5292 | 1300 | 0.8600          | 0.7945   |
| 0.7832        | 2.7237 | 1400 | 0.8134          | 0.8023   |
| 0.7061        | 2.9183 | 1500 | 0.8009          | 0.7984   |
| 0.4784        | 3.1128 | 1600 | 0.7495          | 0.8087   |
| 0.514         | 3.3074 | 1700 | 0.7359          | 0.8101   |
| 0.4407        | 3.5019 | 1800 | 0.7049          | 0.8184   |
| 0.4831        | 3.6965 | 1900 | 0.6899          | 0.8208   |
| 0.4283        | 3.8911 | 2000 | 0.6866          | 0.8286   |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1

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