readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: finetuned-ViT-Indian-Food-Classification-v1
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: Human_Action_Recognition
        type: imagefolder
        config: default
        split: train
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9341126461211477
  ---
  
  <!-- 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-ViT-Indian-Food-Classification-v1
  
  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 Human_Action_Recognition dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2665
  - Accuracy: 0.9341
  
  ## 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: 5
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.2019        | 0.3   | 100  | 0.9317          | 0.8555   |
  | 0.6664        | 0.6   | 200  | 0.5432          | 0.8959   |
  | 0.5096        | 0.9   | 300  | 0.4700          | 0.8990   |
  | 0.6116        | 1.2   | 400  | 0.4504          | 0.8799   |
  | 0.4326        | 1.5   | 500  | 0.3856          | 0.8980   |
  | 0.3349        | 1.8   | 600  | 0.3471          | 0.9129   |
  | 0.5141        | 2.1   | 700  | 0.3708          | 0.9033   |
  | 0.32          | 2.4   | 800  | 0.3338          | 0.9139   |
  | 0.2611        | 2.7   | 900  | 0.3159          | 0.9171   |
  | 0.1836        | 3.0   | 1000 | 0.2696          | 0.9299   |
  | 0.2492        | 3.3   | 1100 | 0.2979          | 0.9214   |
  | 0.1846        | 3.6   | 1200 | 0.3165          | 0.9203   |
  | 0.1505        | 3.9   | 1300 | 0.2806          | 0.9288   |
  | 0.1854        | 4.2   | 1400 | 0.2665          | 0.9341   |
  | 0.124         | 4.5   | 1500 | 0.2695          | 0.9341   |
  | 0.0719        | 4.8   | 1600 | 0.2668          | 0.9320   |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  