transformers tensorboard safetensors vit image-classification generated_from_trainer dataset:imagefolder base_model:google/vit-base-patch16-224-in21k license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
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-indian-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: 0.9980858191099059
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# 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: 0.0144
- Accuracy: 0.9981

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9849        | 0.26  | 100  | 0.8445          | 0.8721   |
| 0.4628        | 0.51  | 200  | 0.4435          | 0.9201   |
| 0.4738        | 0.77  | 300  | 0.3339          | 0.9336   |
| 0.3603        | 1.02  | 400  | 0.2924          | 0.9328   |
| 0.1792        | 1.28  | 500  | 0.1862          | 0.9560   |
| 0.2304        | 1.53  | 600  | 0.1352          | 0.9711   |
| 0.1512        | 1.79  | 700  | 0.1244          | 0.9689   |
| 0.1805        | 2.04  | 800  | 0.0843          | 0.9805   |
| 0.1672        | 2.3   | 900  | 0.0576          | 0.9879   |
| 0.0154        | 2.55  | 1000 | 0.0498          | 0.9900   |
| 0.0357        | 2.81  | 1100 | 0.0359          | 0.9933   |
| 0.0241        | 3.06  | 1200 | 0.0290          | 0.9951   |
| 0.0133        | 3.32  | 1300 | 0.0228          | 0.9967   |
| 0.0088        | 3.57  | 1400 | 0.0193          | 0.9970   |
| 0.0511        | 3.83  | 1500 | 0.0144          | 0.9981   |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
