---
library_name: transformers
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: finetuned-fake-food
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: indian_food_images
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8828996282527881
---

<!-- 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-fake-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: 0.3411
- Accuracy: 0.8829

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5083        | 1.0   | 761  | 0.4226          | 0.8234   |
| 0.5171        | 2.0   | 1522 | 0.4868          | 0.7825   |
| 0.5042        | 3.0   | 2283 | 0.4948          | 0.7862   |
| 0.282         | 4.0   | 3044 | 0.4702          | 0.8067   |
| 0.8396        | 5.0   | 3805 | 0.4037          | 0.8290   |
| 0.2213        | 6.0   | 4566 | 0.5371          | 0.7918   |
| 0.636         | 7.0   | 5327 | 0.4830          | 0.8234   |
| 0.5217        | 8.0   | 6088 | 0.3411          | 0.8829   |
| 0.3796        | 9.0   | 6849 | 0.3674          | 0.8680   |
| 0.286         | 10.0  | 7610 | 0.3989          | 0.8494   |


### Framework versions

- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1