readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: finetuned-SwinT-Indian-Food-Classification-v2
    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: 0.9458023379383634
  ---
  
  <!-- 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-v2
  
  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: 0.2226
  - Accuracy: 0.9458
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.9351        | 0.3   | 100  | 0.6017          | 0.8363   |
  | 0.5667        | 0.6   | 200  | 0.4384          | 0.8767   |
  | 0.5548        | 0.9   | 300  | 0.4215          | 0.8767   |
  | 0.5516        | 1.2   | 400  | 0.4290          | 0.8735   |
  | 0.3782        | 1.5   | 500  | 0.3502          | 0.8980   |
  | 0.3115        | 1.8   | 600  | 0.3780          | 0.8937   |
  | 0.4229        | 2.1   | 700  | 0.3545          | 0.8905   |
  | 0.3832        | 2.4   | 800  | 0.3446          | 0.9086   |
  | 0.2745        | 2.7   | 900  | 0.3299          | 0.9150   |
  | 0.2063        | 3.0   | 1000 | 0.2592          | 0.9277   |
  | 0.2077        | 3.3   | 1100 | 0.3772          | 0.9150   |
  | 0.2041        | 3.6   | 1200 | 0.2855          | 0.9214   |
  | 0.2541        | 3.9   | 1300 | 0.2502          | 0.9330   |
  | 0.1203        | 4.2   | 1400 | 0.2577          | 0.9362   |
  | 0.1594        | 4.5   | 1500 | 0.2226          | 0.9458   |
  | 0.1015        | 4.8   | 1600 | 0.2368          | 0.9437   |
  
  
  ### Framework versions
  
  - Transformers 4.21.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  