readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - food101
  metrics:
  - accuracy
  model-index:
  - name: swin-finetuned-food101
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: food101
        type: food101
        config: default
        split: train
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9220198019801981
  ---
  
  <!-- 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. -->
  
  # swin-finetuned-food101
  
  This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224](https://huggingface.co/microsoft/swin-base-patch4-window7-224) on the food101 dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.4401
  - Accuracy: 0.9220
  
  ## 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: 5e-05
  - train_batch_size: 16
  - eval_batch_size: 16
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 64
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
  | 0.0579        | 1.0   | 1183  | 0.4190          | 0.9102   |
  | 0.0129        | 2.0   | 2366  | 0.4179          | 0.9155   |
  | 0.0076        | 3.0   | 3549  | 0.4219          | 0.9198   |
  | 0.0197        | 4.0   | 4732  | 0.4487          | 0.9160   |
  | 0.0104        | 5.0   | 5915  | 0.4414          | 0.9210   |
  | 0.0007        | 6.0   | 7098  | 0.4401          | 0.9220   |
  | 0.0021        | 7.0   | 8281  | 0.4401          | 0.9220   |
  | 0.0015        | 8.0   | 9464  | 0.4401          | 0.9220   |
  | 0.0056        | 9.0   | 10647 | 0.4401          | 0.9220   |
  | 0.0019        | 10.0  | 11830 | 0.4401          | 0.9220   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  