readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - snacks
  metrics:
  - accuracy
  model-index:
  - name: swin-base-finetuned-snacks
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: snacks
        type: snacks
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9455497382198953
  ---
  
  <!-- 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-base-finetuned-snacks
  
  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 snacks dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.2404
  - Accuracy: 0.9455
  
  ## 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: 32
  - eval_batch_size: 32
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 128
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 3
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.0044        | 1.0   | 38   | 0.2981          | 0.9309   |
  | 0.0023        | 2.0   | 76   | 0.2287          | 0.9445   |
  | 0.0012        | 3.0   | 114  | 0.2404          | 0.9455   |
  
  
  ### Framework versions
  
  - Transformers 4.19.2
  - Pytorch 1.11.0+cu113
  - Datasets 2.2.2
  - Tokenizers 0.12.1
  