readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: vit-base-tour-demo-v5
    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-tour-demo-v5
  
  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.1467
  - Accuracy: 0.4880
  
  ## 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: 4
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 3.4573        | 0.13  | 100  | 3.2038          | 0.3334   |
  | 2.9547        | 0.27  | 200  | 2.8725          | 0.3672   |
  | 2.6093        | 0.4   | 300  | 2.7619          | 0.3954   |
  | 2.6212        | 0.54  | 400  | 2.6269          | 0.3942   |
  | 2.5063        | 0.67  | 500  | 2.5060          | 0.4211   |
  | 2.3113        | 0.81  | 600  | 2.5348          | 0.4201   |
  | 2.5702        | 0.94  | 700  | 2.3345          | 0.4502   |
  | 2.0479        | 1.08  | 800  | 2.3183          | 0.4484   |
  | 1.754         | 1.21  | 900  | 2.2546          | 0.4661   |
  | 1.7772        | 1.34  | 1000 | 2.1994          | 0.4794   |
  | 1.9276        | 1.48  | 1100 | 2.1672          | 0.4731   |
  | 1.6621        | 1.61  | 1200 | 2.1676          | 0.4845   |
  | 1.7063        | 1.75  | 1300 | 2.1446          | 0.4806   |
  | 1.8655        | 1.88  | 1400 | 2.1121          | 0.4933   |
  | 1.4577        | 2.02  | 1500 | 2.0934          | 0.4955   |
  | 1.1857        | 2.15  | 1600 | 2.1128          | 0.4906   |
  | 1.1684        | 2.28  | 1700 | 2.1218          | 0.4941   |
  | 1.3873        | 2.42  | 1800 | 2.1108          | 0.4957   |
  | 1.3545        | 2.55  | 1900 | 2.0985          | 0.4992   |
  | 0.9789        | 2.69  | 2000 | 2.0997          | 0.4961   |
  | 1.1772        | 2.82  | 2100 | 2.1141          | 0.4951   |
  | 1.0968        | 2.96  | 2200 | 2.1097          | 0.4922   |
  | 0.7883        | 3.09  | 2300 | 2.1170          | 0.5067   |
  | 0.7593        | 3.23  | 2400 | 2.1516          | 0.4847   |
  | 0.5671        | 3.36  | 2500 | 2.1414          | 0.4925   |
  | 0.6442        | 3.49  | 2600 | 2.1498          | 0.4880   |
  | 0.516         | 3.63  | 2700 | 2.1442          | 0.4878   |
  | 0.6283        | 3.76  | 2800 | 2.1518          | 0.4882   |
  | 0.5629        | 3.9   | 2900 | 2.1467          | 0.4880   |
  
  
  ### Framework versions
  
  - Transformers 4.22.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.1
  - Tokenizers 0.12.1
  