readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: vit-base-patch16-224
    results: []
  ---
  
  <!-- 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. -->
  
  # vit-base-patch16-224
  
  This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.2817
  - Accuracy: 0.7205
  
  ## 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: 0.001
  - 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: 10
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.8025        | 0.98  | 27   | 0.6852          | 0.6407   |
  | 0.5648        | 1.98  | 54   | 0.7587          | 0.6971   |
  | 0.2165        | 2.98  | 81   | 0.6410          | 0.7387   |
  | 0.0587        | 3.98  | 108  | 1.9350          | 0.5682   |
  | 0.041         | 4.98  | 135  | 0.9925          | 0.7348   |
  | 0.013         | 5.98  | 162  | 1.3159          | 0.6980   |
  | 0.025         | 6.98  | 189  | 1.4855          | 0.7456   |
  | 0.0243        | 7.98  | 216  | 1.4230          | 0.7489   |
  | 0.0016        | 8.98  | 243  | 1.2937          | 0.7117   |
  | 0.0026        | 9.98  | 270  | 1.2817          | 0.7205   |
  
  
  ### Framework versions
  
  - Transformers 4.25.1
  - Pytorch 1.13.0+cu116
  - Datasets 2.7.1
  - Tokenizers 0.13.2
  