---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: finetuned-indian-food
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9543039319872476
---

<!-- 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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1918
- Accuracy: 0.9543

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0175        | 0.3   | 100  | 0.9247          | 0.8629   |
| 0.7418        | 0.6   | 200  | 0.5536          | 0.8990   |
| 0.6652        | 0.9   | 300  | 0.4036          | 0.9182   |
| 0.5959        | 1.2   | 400  | 0.4022          | 0.8980   |
| 0.4478        | 1.5   | 500  | 0.3247          | 0.9288   |
| 0.4717        | 1.8   | 600  | 0.3019          | 0.9267   |
| 0.34          | 2.1   | 700  | 0.2594          | 0.9352   |
| 0.3518        | 2.4   | 800  | 0.2507          | 0.9352   |
| 0.3352        | 2.7   | 900  | 0.2484          | 0.9426   |
| 0.2493        | 3.0   | 1000 | 0.2266          | 0.9394   |
| 0.2034        | 3.3   | 1100 | 0.2011          | 0.9479   |
| 0.1753        | 3.6   | 1200 | 0.2089          | 0.9447   |
| 0.1614        | 3.9   | 1300 | 0.1918          | 0.9543   |


### Framework versions

- Transformers 4.21.0
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1