readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - generated_from_trainer
  metrics:
  - f1
  model-index:
  - name: vit-base-tour-augmentation-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-augmentation-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 the beans dataset.
  It achieves the following results on the evaluation set:
  - Loss: 2.4590
  - Acc: {'accuracy': 0.42896389324960754}
  - F1: {'f1': 0.4271599947085357}
  
  ## 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: 32
  - eval_batch_size: 8
  - seed: 42
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 8
  - mixed_precision_training: Native AMP
  
  ### Training results
  
  | Training Loss | Epoch | Step  | Validation Loss | Acc                               | F1                          |
  |:-------------:|:-----:|:-----:|:---------------:|:---------------------------------:|:---------------------------:|
  | 1.634         | 0.77  | 1000  | 2.4590          | {'accuracy': 0.42896389324960754} | {'f1': 0.4271599947085357}  |
  | 0.8261        | 1.53  | 2000  | 2.4812          | {'accuracy': 0.4703689167974882}  | {'f1': 0.4487847422578378}  |
  | 0.3823        | 2.3   | 3000  | 2.6315          | {'accuracy': 0.46683673469387754} | {'f1': 0.45668802662482005} |
  | 0.1652        | 3.07  | 4000  | 2.8592          | {'accuracy': 0.46703296703296704} | {'f1': 0.4525782242029193}  |
  | 0.0713        | 3.83  | 5000  | 3.0906          | {'accuracy': 0.4430926216640502}  | {'f1': 0.45342569349779865} |
  | 0.0354        | 4.6   | 6000  | 3.2511          | {'accuracy': 0.45506279434850866} | {'f1': 0.44957410984221347} |
  | 0.0214        | 5.36  | 7000  | 3.3369          | {'accuracy': 0.47370486656200944} | {'f1': 0.4603765751991713}  |
  | 0.0129        | 6.13  | 8000  | 3.4611          | {'accuracy': 0.4619309262166405}  | {'f1': 0.4624748087985776}  |
  | 0.0079        | 6.9   | 9000  | 3.5376          | {'accuracy': 0.46251962323390894} | {'f1': 0.4584329789658534}  |
  | 0.0058        | 7.66  | 10000 | 3.5842          | {'accuracy': 0.4705651491365777}  | {'f1': 0.46144792853832145} |
  
  
  ### Framework versions
  
  - Transformers 4.22.2
  - Pytorch 1.12.1+cu113
  - Datasets 2.5.2
  - Tokenizers 0.12.1
  