readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - cifar100
  metrics:
  - accuracy
  model-index:
  - name: swin-base-finetuned-cifar100
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: cifar100
        type: cifar100
        config: cifar100
        split: train
        args: cifar100
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.9201
  ---
  
  <!-- 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-cifar100
  
  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 cifar100 dataset.
  It achieves the following results on the evaluation set:
  - Accuracy: 0.9201
  - Loss: 0.3670
  
  ## 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: 4e-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: 5
  
  ### Training results
  
  | Training Loss | Epoch | Step | Accuracy | Validation Loss |
  |:-------------:|:-----:|:----:|:--------:|:---------------:|
  | 0.3536        | 1.0   | 781  | 0.9052   | 0.3141          |
  | 0.3254        | 2.0   | 1562 | 0.9117   | 0.2991          |
  | 0.0936        | 3.0   | 2343 | 0.9138   | 0.3322          |
  | 0.1054        | 4.0   | 3124 | 0.9158   | 0.3483          |
  | 0.0269        | 5.0   | 3905 | 0.9201   | 0.3670          |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.8.0
  - Tokenizers 0.13.2
  