---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
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 an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2293
- Accuracy: 0.9405
- Precision: 0.9395
- Recall: 0.9420
- F1: 0.9402

## 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 | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.8589        | 0.3   | 100  | 0.5618          | 0.8714   | 0.8981    | 0.8620 | 0.8696 |
| 0.6973        | 0.6   | 200  | 0.5544          | 0.8608   | 0.8742    | 0.8690 | 0.8630 |
| 0.4078        | 0.9   | 300  | 0.4671          | 0.8831   | 0.8915    | 0.8840 | 0.8812 |
| 0.3818        | 1.2   | 400  | 0.4203          | 0.8884   | 0.9017    | 0.8864 | 0.8877 |
| 0.2262        | 1.5   | 500  | 0.3481          | 0.9107   | 0.9177    | 0.9085 | 0.9098 |
| 0.2137        | 1.8   | 600  | 0.3761          | 0.9022   | 0.9094    | 0.9027 | 0.9026 |
| 0.4515        | 2.1   | 700  | 0.3722          | 0.9044   | 0.9091    | 0.9041 | 0.9017 |
| 0.3024        | 2.4   | 800  | 0.3105          | 0.9203   | 0.9198    | 0.9220 | 0.9188 |
| 0.1748        | 2.7   | 900  | 0.2767          | 0.9288   | 0.9274    | 0.9293 | 0.9272 |
| 0.1959        | 3.0   | 1000 | 0.2825          | 0.9256   | 0.9318    | 0.9243 | 0.9230 |
| 0.1663        | 3.3   | 1100 | 0.2549          | 0.9341   | 0.9362    | 0.9366 | 0.9356 |
| 0.0513        | 3.6   | 1200 | 0.2254          | 0.9416   | 0.9436    | 0.9422 | 0.9424 |
| 0.1478        | 3.9   | 1300 | 0.2293          | 0.9405   | 0.9395    | 0.9420 | 0.9402 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2