readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - f1
  model-index:
  - name: cvt-21-finetuned-brs2
    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: 0.660377358490566
      - name: F1
        type: f1
        value: 0.608695652173913
  ---
  
  <!-- 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. -->
  
  # cvt-21-finetuned-brs2
  
  This model is a fine-tuned version of [microsoft/cvt-21](https://huggingface.co/microsoft/cvt-21) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 0.6947
  - Accuracy: 0.6604
  - F1: 0.6087
  - Precision (ppv): 0.5385
  - Recall (sensitivity): 0.7
  - Specificity: 0.6364
  - Npv: 0.7778
  - Auc: 0.6682
  
  ## 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    |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------------:|:--------------------:|:-----------:|:------:|:------:|
  | 0.8177        | 1.89  | 100  | 0.7113          | 0.5283   | 0.5098 | 0.4194          | 0.65                 | 0.4545      | 0.6818 | 0.5523 |
  | 0.736         | 3.77  | 200  | 0.7178          | 0.5283   | 0.3902 | 0.3810          | 0.4                  | 0.6061      | 0.625  | 0.5030 |
  | 0.5978        | 5.66  | 300  | 0.6889          | 0.6038   | 0.5532 | 0.4815          | 0.65                 | 0.5758      | 0.7308 | 0.6129 |
  | 0.5576        | 7.55  | 400  | 0.7349          | 0.4717   | 0.5484 | 0.4048          | 0.85                 | 0.2424      | 0.7273 | 0.5462 |
  | 0.5219        | 9.43  | 500  | 0.6522          | 0.6038   | 0.4    | 0.4667          | 0.35                 | 0.7576      | 0.6579 | 0.5538 |
  | 0.5326        | 11.32 | 600  | 0.6665          | 0.6226   | 0.5238 | 0.5             | 0.55                 | 0.6667      | 0.7097 | 0.6083 |
  | 0.4381        | 13.21 | 700  | 0.7685          | 0.4717   | 0.5333 | 0.4             | 0.8                  | 0.2727      | 0.6923 | 0.5364 |
  | 0.5598        | 15.09 | 800  | 0.7212          | 0.5283   | 0.1935 | 0.2727          | 0.15                 | 0.7576      | 0.5952 | 0.4538 |
  | 0.6887        | 16.98 | 900  | 0.6985          | 0.6604   | 0.64   | 0.5333          | 0.8                  | 0.5758      | 0.8261 | 0.6879 |
  | 0.7594        | 18.87 | 1000 | 0.7040          | 0.5472   | 0.4286 | 0.4091          | 0.45                 | 0.6061      | 0.6452 | 0.5280 |
  | 0.2177        | 20.75 | 1100 | 0.8056          | 0.4528   | 0.5397 | 0.3953          | 0.85                 | 0.2121      | 0.7    | 0.5311 |
  | 0.4893        | 22.64 | 1200 | 0.8821          | 0.3396   | 0.3860 | 0.2973          | 0.55                 | 0.2121      | 0.4375 | 0.3811 |
  | 0.5994        | 24.53 | 1300 | 0.8059          | 0.5660   | 0.5660 | 0.4545          | 0.75                 | 0.4545      | 0.75   | 0.6023 |
  | 0.5179        | 26.42 | 1400 | 0.6750          | 0.6038   | 0.4615 | 0.4737          | 0.45                 | 0.6970      | 0.6765 | 0.5735 |
  | 0.198         | 28.3  | 1500 | 0.7448          | 0.3962   | 0.3333 | 0.2857          | 0.4                  | 0.3939      | 0.52   | 0.3970 |
  | 0.6536        | 30.19 | 1600 | 0.7555          | 0.5094   | 0.4583 | 0.3929          | 0.55                 | 0.4848      | 0.64   | 0.5174 |
  | 0.7558        | 32.08 | 1700 | 0.6664          | 0.5849   | 0.4762 | 0.4545          | 0.5                  | 0.6364      | 0.6774 | 0.5682 |
  | 0.4915        | 33.96 | 1800 | 0.9213          | 0.3962   | 0.5152 | 0.3696          | 0.85                 | 0.1212      | 0.5714 | 0.4856 |
  | 0.3661        | 35.85 | 1900 | 0.9202          | 0.4528   | 0.4912 | 0.3784          | 0.7                  | 0.3030      | 0.625  | 0.5015 |
  | 0.4838        | 37.74 | 2000 | 0.9297          | 0.4528   | 0.5085 | 0.3846          | 0.75                 | 0.2727      | 0.6429 | 0.5114 |
  | 0.8461        | 39.62 | 2100 | 0.9464          | 0.4717   | 0.5758 | 0.4130          | 0.95                 | 0.1818      | 0.8571 | 0.5659 |
  | 0.6937        | 41.51 | 2200 | 0.7129          | 0.5094   |