readme: ---
  license: apache-2.0
  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: 0.9330499468650372
  widget:
  - src: https://huggingface.co/rajistics/finetuned-indian-food/resolve/main/003.jpg
    example_title: fried_rice
  - src: https://huggingface.co/rajistics/finetuned-indian-food/resolve/main/126.jpg
    example_title: paani_puri
  - src: https://huggingface.co/rajistics/finetuned-indian-food/resolve/main/401.jpg
    example_title: chapati
  ---
  
  <!-- 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: 0.2632
  - Accuracy: 0.9330
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.1794        | 0.3   | 100  | 0.9208          | 0.8565   |
  | 0.6513        | 0.6   | 200  | 0.5410          | 0.8842   |
  | 0.5904        | 0.9   | 300  | 0.4978          | 0.8799   |
  | 0.4461        | 1.2   | 400  | 0.3669          | 0.9192   |
  | 0.5633        | 1.5   | 500  | 0.4340          | 0.8842   |
  | 0.2489        | 1.8   | 600  | 0.3355          | 0.9171   |
  | 0.3171        | 2.1   | 700  | 0.3286          | 0.9192   |
  | 0.3785        | 2.4   | 800  | 0.3232          | 0.9171   |
  | 0.2278        | 2.7   | 900  | 0.3338          | 0.9192   |
  | 0.0894        | 3.0   | 1000 | 0.2870          | 0.9245   |
  | 0.2092        | 3.3   | 1100 | 0.2884          | 0.9288   |
  | 0.1466        | 3.6   | 1200 | 0.2673          | 0.9320   |
  | 0.1789        | 3.9   | 1300 | 0.2632          | 0.9330   |
  
  
  ### Framework versions
  
  - Transformers 4.21.1
  - Pytorch 1.12.0+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  