---
license: apache-2.0
base_model: Zannatul/final_withAug-google-vit-base-patch16-384-in21k-batch_16_epoch_4_classes_24
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: google-vit-base-patch16-384-in21k-batch_16_epoch_4_classes_24_final_withAug_12th_May
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: bengali_food_images
      type: imagefolder
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9904891304347826
---

<!-- 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. -->

# google-vit-base-patch16-384-in21k-batch_16_epoch_4_classes_24_final_withAug_12th_May

This model is a fine-tuned version of [Zannatul/final_withAug-google-vit-base-patch16-384-in21k-batch_16_epoch_4_classes_24](https://huggingface.co./Zannatul/final_withAug-google-vit-base-patch16-384-in21k-batch_16_epoch_4_classes_24) on the bengali_food_images dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0282
- Accuracy: 0.9905

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.269         | 0.09  | 100  | 0.4001          | 0.8723   |
| 0.1829        | 0.17  | 200  | 0.0928          | 0.9728   |
| 0.1737        | 0.26  | 300  | 0.1615          | 0.9457   |
| 0.2096        | 0.34  | 400  | 0.4138          | 0.9022   |
| 0.1855        | 0.43  | 500  | 0.1814          | 0.9511   |
| 0.0901        | 0.51  | 600  | 0.1435          | 0.9579   |
| 0.1406        | 0.6   | 700  | 0.1468          | 0.9620   |
| 0.136         | 0.68  | 800  | 0.1532          | 0.9565   |
| 0.0666        | 0.77  | 900  | 0.1177          | 0.9674   |
| 0.1145        | 0.85  | 1000 | 0.1794          | 0.9497   |
| 0.0865        | 0.94  | 1100 | 0.1113          | 0.9688   |
| 0.0612        | 1.02  | 1200 | 0.1270          | 0.9688   |
| 0.0038        | 1.11  | 1300 | 0.0724          | 0.9783   |
| 0.0006        | 1.19  | 1400 | 0.0715          | 0.9851   |
| 0.0007        | 1.28  | 1500 | 0.0616          | 0.9796   |
| 0.0579        | 1.36  | 1600 | 0.1259          | 0.9715   |
| 0.0009        | 1.45  | 1700 | 0.1028          | 0.9755   |
| 0.0295        | 1.53  | 1800 | 0.0637          | 0.9823   |
| 0.0484        | 1.62  | 1900 | 0.0893          | 0.9783   |
| 0.0371        | 1.71  | 2000 | 0.0637          | 0.9837   |
| 0.0359        | 1.79  | 2100 | 0.0389          | 0.9878   |
| 0.0006        | 1.88  | 2200 | 0.0750          | 0.9823   |
| 0.0189        | 1.96  | 2300 | 0.0451          | 0.9851   |
| 0.0442        | 2.05  | 2400 | 0.0772          | 0.9796   |
| 0.0006        | 2.13  | 2500 | 0.1988          | 0.9620   |
| 0.006         | 2.22  | 2600 | 0.0659          | 0.9864   |
| 0.0093        | 2.3   | 2700 | 0.0754          | 0.9810   |
| 0.0008        | 2.39  | 2800 | 0.0800          | 0.9783   |
| 0.0003        | 2.47  | 2900 | 0.0617          | 0.9864   |
| 0.0094        | 2.56  | 3000 | 0.0736          | 0.9837   |
| 0.0001        | 2.64  | 3100 | 0.0538          | 0.9823   |
| 0.001         | 2.73  | 3200 | 0.0606          | 0.9878   |
| 0.0001        | 2.81  | 3300 | 0.0433          | 0.9864   |
| 0.0001        | 2.9   | 3400 | 0.0583          | 0.9823   |
| 0.0001        | 2.98  | 3500 | 0.0388          | 0.9905   |
| 0.0001        | 3.07  | 3600 | 0.0408          | 0.9891   |
| 0.0001        | 3.15  | 3700 | 0.0375          | 0.9891   |
| 0.0001        | 3.24  | 3800 | 0.0367          | 0.9878   |
| 0.0001        | 3.32  | 3900 | 0.0355          | 0.9878   |
| 0.0001        | 3.41  | 4000 | 0.0395          | 0.9878   |
| 0.0001        | 3.5   | 4100 | 0.0382          | 0.9878   |
| 0.0001        | 3.58  | 4200 | 0.0399          | 0.9891   |
| 0.0001        | 3.67  | 4300 | 0.0396          | 0.9891   |
| 0.0072        | 3.75  | 4400 | 0.0355          | 0.9905   |
| 0.0001        | 3.84  | 4500 | 0.0284          | 0.9918   |
| 0.0001        | 3.92  | 4600 | 0.0282          | 0.9905   |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2