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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 indian_food_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0026
- Accuracy: 0.9996

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7056        | 0.1   | 100  | 0.5113          | 0.8881   |
| 0.3027        | 0.21  | 200  | 0.1280          | 0.9796   |
| 0.2823        | 0.31  | 300  | 0.1580          | 0.9656   |
| 0.3273        | 0.42  | 400  | 0.0879          | 0.9837   |
| 0.1808        | 0.52  | 500  | 0.0812          | 0.9822   |
| 0.2101        | 0.63  | 600  | 0.0339          | 0.9937   |
| 0.1495        | 0.73  | 700  | 0.0568          | 0.9833   |
| 0.1296        | 0.84  | 800  | 0.0629          | 0.9844   |
| 0.1462        | 0.94  | 900  | 0.0886          | 0.9733   |
| 0.0519        | 1.04  | 1000 | 0.0544          | 0.9870   |
| 0.3192        | 1.15  | 1100 | 0.0892          | 0.9726   |
| 0.158         | 1.25  | 1200 | 0.0632          | 0.98     |
| 0.0266        | 1.36  | 1300 | 0.0233          | 0.9944   |
| 0.1832        | 1.46  | 1400 | 0.0292          | 0.9930   |
| 0.1212        | 1.57  | 1500 | 0.0489          | 0.9852   |
| 0.0994        | 1.67  | 1600 | 0.0142          | 0.9974   |
| 0.0219        | 1.78  | 1700 | 0.0277          | 0.9930   |
| 0.0664        | 1.88  | 1800 | 0.0158          | 0.9974   |
| 0.0834        | 1.99  | 1900 | 0.0124          | 0.9978   |
| 0.1093        | 2.09  | 2000 | 0.0140          | 0.9974   |
| 0.1726        | 2.19  | 2100 | 0.0147          | 0.9963   |
| 0.0476        | 2.3   | 2200 | 0.0058          | 0.9993   |
| 0.0257        | 2.4   | 2300 | 0.0424          | 0.9911   |
| 0.0215        | 2.51  | 2400 | 0.0076          | 0.9989   |
| 0.0748        | 2.61  | 2500 | 0.0099          | 0.9974   |
| 0.0059        | 2.72  | 2600 | 0.0053          | 0.9993   |
| 0.0527        | 2.82  | 2700 | 0.0149          | 0.9963   |
| 0.0203        | 2.93  | 2800 | 0.0041          | 0.9993   |
| 0.0791        | 3.03  | 2900 | 0.0033          | 0.9989   |
| 0.0389        | 3.13  | 3000 | 0.0033          | 0.9989   |
| 0.0459        | 3.24  | 3100 | 0.0044          | 0.9989   |
| 0.0276        | 3.34  | 3200 | 0.0031          | 0.9996   |
| 0.0139        | 3.45  | 3300 | 0.0028          | 0.9996   |
| 0.0076        | 3.55  | 3400 | 0.0055          | 0.9985   |
| 0.0097        | 3.66  | 3500 | 0.0027          | 0.9996   |
| 0.0193        | 3.76  | 3600 | 0.0026          | 0.9996   |
| 0.0471        | 3.87  | 3700 | 0.0027          | 0.9996   |
| 0.0282        | 3.97  | 3800 | 0.0027          | 0.9996   |


### Framework versions

- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3

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