readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  metrics:
  - accuracy
  model-index:
  - name: vit-large-dataset-model-v3
    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-large-dataset-model-v3
  
  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.0630
  - Accuracy: 0.9850
  
  ## 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 |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 0.0465        | 0.36  | 500  | 0.1289          | 0.9612   |
  | 0.0253        | 0.71  | 1000 | 0.0983          | 0.9693   |
  | 0.008         | 1.07  | 1500 | 0.0957          | 0.9728   |
  | 0.0569        | 1.43  | 2000 | 0.0668          | 0.9793   |
  | 0.035         | 1.79  | 2500 | 0.0865          | 0.9752   |
  | 0.0034        | 2.14  | 3000 | 0.0748          | 0.9773   |
  | 0.0638        | 2.5   | 3500 | 0.0708          | 0.9805   |
  | 0.0195        | 2.86  | 4000 | 0.0782          | 0.9821   |
  | 0.0012        | 3.21  | 4500 | 0.0739          | 0.9820   |
  | 0.0013        | 3.57  | 5000 | 0.0680          | 0.9845   |
  | 0.0417        | 3.93  | 5500 | 0.0630          | 0.9850   |
  
  
  ### Framework versions
  
  - Transformers 4.22.0.dev0
  - Pytorch 1.12.1+cu113
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  