readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: finetuned-SwinT-Indian-Food-Classification-v3
    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: 0.9436769394261424
  ---
  
  <!-- 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-SwinT-Indian-Food-Classification-v3
  
  This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the Indian-Food-Images dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2910
  - Accuracy: 0.9437
  
  ## 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: 10
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.9511        | 0.3   | 100  | 0.6092          | 0.8172   |
  | 0.6214        | 0.6   | 200  | 0.4406          | 0.8672   |
  | 0.7355        | 0.9   | 300  | 0.3665          | 0.8927   |
  | 0.6078        | 1.2   | 400  | 0.3285          | 0.9065   |
  | 0.439         | 1.5   | 500  | 0.3855          | 0.8916   |
  | 0.3644        | 1.8   | 600  | 0.4082          | 0.8969   |
  | 0.4748        | 2.1   | 700  | 0.3496          | 0.9022   |
  | 0.3966        | 2.4   | 800  | 0.3626          | 0.8905   |
  | 0.5799        | 2.7   | 900  | 0.4833          | 0.8767   |
  | 0.2995        | 3.0   | 1000 | 0.3387          | 0.9044   |
  | 0.3152        | 3.3   | 1100 | 0.3739          | 0.9097   |
  | 0.3284        | 3.6   | 1200 | 0.4217          | 0.8916   |
  | 0.3631        | 3.9   | 1300 | 0.4118          | 0.9044   |
  | 0.219         | 4.2   | 1400 | 0.3721          | 0.9139   |
  | 0.2874        | 4.5   | 1500 | 0.3030          | 0.9288   |
  | 0.2819        | 4.8   | 1600 | 0.4056          | 0.9150   |
  | 0.1755        | 5.11  | 1700 | 0.4039          | 0.9097   |
  | 0.2462        | 5.41  | 1800 | 0.3550          | 0.9118   |
  | 0.1737        | 5.71  | 1900 | 0.3444          | 0.9150   |
  | 0.174         | 6.01  | 2000 | 0.3667          | 0.9160   |
  | 0.1536        | 6.31  | 2100 | 0.3301          | 0.9288   |
  | 0.0911        | 6.61  | 2200 | 0.3390          | 0.9299   |
  | 0.0907        | 6.91  | 2300 | 0.2923          | 0.9288   |
  | 0.0921        | 7.21  | 2400 | 0.3502          | 0.9256   |
  | 0.1662        | 7.51  | 2500 | 0.3197          | 0.9341   |
  | 0.0607        | 7.81  | 2600 | 0.3092          | 0.9362   |
  | 0.111         | 8.11  | 2700 | 0.3146          | 0.9394   |
  | 0.0588        | 8.41  | 2800 | 0.3069          | 0.9341   |
  | 0.131         | 8.71  | 2900 | 0.2971          | 0.9405   |
  | 0.1903        | 9.01  | 3000 | 0.3078          | 0.9384   |
  | 0.2116        | 9.31  | 3100 | 0.3112          | 0.9341   |
  | 0.1415        | 9.61  | 3200 | 0.2956          | 0.9405   |
  | 0.1106        | 9.91  | 3300 | 0.2910          | 0.9437   |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  