---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned-indian-food
  results: []
---

<!-- 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. -->

# 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: 0.2867
- Accuracy: 0.9267

## 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.0192        | 0.3003 | 100  | 0.9248          | 0.8480   |
| 0.635         | 0.6006 | 200  | 0.5917          | 0.8863   |
| 0.6523        | 0.9009 | 300  | 0.5134          | 0.8799   |
| 0.4247        | 1.2012 | 400  | 0.3983          | 0.9044   |
| 0.4393        | 1.5015 | 500  | 0.4119          | 0.8980   |
| 0.4631        | 1.8018 | 600  | 0.3752          | 0.9107   |
| 0.2992        | 2.1021 | 700  | 0.3469          | 0.9129   |
| 0.3           | 2.4024 | 800  | 0.3157          | 0.9203   |
| 0.2372        | 2.7027 | 900  | 0.3210          | 0.9192   |
| 0.2447        | 3.0030 | 1000 | 0.3140          | 0.9224   |
| 0.2209        | 3.3033 | 1100 | 0.3034          | 0.9160   |
| 0.2641        | 3.6036 | 1200 | 0.2896          | 0.9277   |
| 0.0954        | 3.9039 | 1300 | 0.2867          | 0.9267   |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1