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---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
- rajistics/indian_food_images
metrics:
- accuracy
widget:
- src: https://huggingface.co/rajistics/finetuned-indian-food/resolve/main/003.jpg
example_title: Example1
model-index:
- name: finetuned-indian-food
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: indian_food_images
type: imagefolder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9521785334750266
---
<!-- 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: 0.2139
- Accuracy: 0.9522
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0846 | 0.3 | 100 | 0.9561 | 0.8555 |
| 0.7894 | 0.6 | 200 | 0.5871 | 0.8927 |
| 0.6233 | 0.9 | 300 | 0.4447 | 0.9107 |
| 0.3619 | 1.2 | 400 | 0.4355 | 0.8937 |
| 0.34 | 1.5 | 500 | 0.3712 | 0.9118 |
| 0.3413 | 1.8 | 600 | 0.4088 | 0.8916 |
| 0.3619 | 2.1 | 700 | 0.3741 | 0.9044 |
| 0.2135 | 2.4 | 800 | 0.3286 | 0.9160 |
| 0.2166 | 2.7 | 900 | 0.2758 | 0.9416 |
| 0.1557 | 3.0 | 1000 | 0.2679 | 0.9330 |
| 0.1115 | 3.3 | 1100 | 0.2529 | 0.9362 |
| 0.1571 | 3.6 | 1200 | 0.2360 | 0.9469 |
| 0.1079 | 3.9 | 1300 | 0.2139 | 0.9522 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
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