readme: ---
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - f1
  model-index:
  - name: dit-base-finetuned-brs
    results:
    - task:
        name: Image Classification
        type: image-classification
      dataset:
        name: imagefolder
        type: imagefolder
        config: default
        split: train
        args: default
      metrics:
      - name: Accuracy
        type: accuracy
        value: 0.8823529411764706
      - name: F1
        type: f1
        value: 0.8571428571428571
  ---
  
  <!-- 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. -->
  
  # dit-base-finetuned-brs
  
  This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.8748
  - Accuracy: 0.8824
  - F1: 0.8571
  - Precision (ppv): 0.8571
  - Recall (sensitivity): 0.8571
  - Specificity: 0.9
  - Npv: 0.9
  - Auc: 0.8786
  
  ## 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: 1e-05
  - train_batch_size: 1
  - eval_batch_size: 1
  - seed: 42
  - gradient_accumulation_steps: 4
  - total_train_batch_size: 4
  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  - lr_scheduler_type: linear
  - lr_scheduler_warmup_ratio: 0.1
  - num_epochs: 100
  
  ### Training results
  
  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision (ppv) | Recall (sensitivity) | Specificity | Npv    | Auc    |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------------:|:--------------------:|:-----------:|:------:|:------:|
  | 0.6624        | 6.25  | 100  | 0.5548          | 0.8235   | 0.7692 | 0.8333          | 0.7143               | 0.9         | 0.8182 | 0.8071 |
  | 0.5201        | 12.49 | 200  | 0.4617          | 0.8824   | 0.8571 | 0.8571          | 0.8571               | 0.9         | 0.9    | 0.8786 |
  | 0.5172        | 18.74 | 300  | 0.4249          | 0.8235   | 0.8000 | 0.75            | 0.8571               | 0.8         | 0.8889 | 0.8286 |
  | 0.4605        | 24.98 | 400  | 0.3172          | 0.8235   | 0.7692 | 0.8333          | 0.7143               | 0.9         | 0.8182 | 0.8071 |
  | 0.4894        | 31.25 | 500  | 0.4466          | 0.8235   | 0.7692 | 0.8333          | 0.7143               | 0.9         | 0.8182 | 0.8071 |
  | 0.3694        | 37.49 | 600  | 0.5077          | 0.8235   | 0.7692 | 0.8333          | 0.7143               | 0.9         | 0.8182 | 0.8071 |
  | 0.6172        | 43.74 | 700  | 0.5722          | 0.7647   | 0.7143 | 0.7143          | 0.7143               | 0.8         | 0.8    | 0.7571 |
  | 0.3671        | 49.98 | 800  | 0.7006          | 0.7647   | 0.6667 | 0.8             | 0.5714               | 0.9         | 0.75   | 0.7357 |
  | 0.4109        | 56.25 | 900  | 0.4410          | 0.8235   | 0.7692 | 0.8333          | 0.7143               | 0.9         | 0.8182 | 0.8071 |
  | 0.3198        | 62.49 | 1000 | 0.7226          | 0.8235   | 0.7692 | 0.8333          | 0.7143               | 0.9         | 0.8182 | 0.8071 |
  | 0.4283        | 68.74 | 1100 | 0.8089          | 0.8235   | 0.7692 | 0.8333          | 0.7143               | 0.9         | 0.8182 | 0.8071 |
  | 0.3273        | 74.98 | 1200 | 0.9059          | 0.7647   | 0.6667 | 0.8             | 0.5714               | 0.9         | 0.75   | 0.7357 |
  | 0.3237        | 81.25 | 1300 | 0.8520          | 0.8235   | 0.7692 | 0.8333          | 0.7143               | 0.9         | 0.8182 | 0.8071 |
  | 0.2014        | 87.49 | 1400 | 0.9183          | 0.7647   | 0.6667 | 0.8             | 0.5714               | 0.9         | 0.75   | 0.7357 |
  | 0.3204        | 93.74 | 1500 | 0.6769          | 0.8824   | 0.8571 | 0.8571          | 0.8571               | 0.9         | 0.9    | 0.8786 |
  | 0.1786        | 99.98 | 1600 | 0.8748          | 0.8824   | 0.8571 | 0.8571          | 0.8571               | 0.9         | 0.9    | 0.8786 |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  