readme: ---
  license: apache-2.0
  tags:
  - image-classification
  - vision
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  model-index:
  - name: vit-base-roman-numeral
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: farleyknight/roman_numerals
        type: imagefolder
        config: default
        split: train
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8308823529411765
  ---
  
  <!-- 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-roman-numeral
  
  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 farleyknight/roman_numerals dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6891
  - Accuracy: 0.8309
  
  ## 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: 2e-05
  - train_batch_size: 8
  - eval_batch_size: 8
  - seed: 1337
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - num_epochs: 5.0
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|
  | 1.9053        | 1.0   | 289  | 1.3241          | 0.7108   |
  | 1.3293        | 2.0   | 578  | 0.9333          | 0.7892   |
  | 1.1251        | 3.0   | 867  | 0.7989          | 0.7843   |
  | 0.9837        | 4.0   | 1156 | 0.6956          | 0.8186   |
  | 0.999         | 5.0   | 1445 | 0.6891          | 0.8309   |
  
  
  ### Framework versions
  
  - Transformers 4.22.0.dev0
  - Pytorch 1.11.0a0+17540c5
  - Datasets 2.4.0
  - Tokenizers 0.12.1
  