readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - f1
  model-index:
  - name: convnext-tiny-224-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: 0.7924528301886793
      - name: F1
        type: f1
        value: 0.7555555555555556
  ---
  
  <!-- 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. -->
  
  # convnext-tiny-224-finetuned-brs2
  
  This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
  It achieves the following results on the evaluation set:
  - Loss: 1.2502
  - Accuracy: 0.7925
  - F1: 0.7556
  - Precision (ppv): 0.8095
  - Recall (sensitivity): 0.7083
  - Specificity: 0.8621
  - Npv: 0.7812
  - Auc: 0.7852
  
  ## 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    |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------------:|:--------------------:|:-----------:|:------:|:------:|
  | 0.6884        | 1.89  | 100  | 0.6907          | 0.5472   | 0.4286 | 0.5             | 0.375                | 0.6897      | 0.5714 | 0.5323 |
  | 0.5868        | 3.77  | 200  | 0.6604          | 0.6415   | 0.4242 | 0.7778          | 0.2917               | 0.9310      | 0.6136 | 0.6114 |
  | 0.4759        | 5.66  | 300  | 0.6273          | 0.6604   | 0.5    | 0.75            | 0.375                | 0.8966      | 0.6341 | 0.6358 |
  | 0.3599        | 7.55  | 400  | 0.6520          | 0.6604   | 0.5    | 0.75            | 0.375                | 0.8966      | 0.6341 | 0.6358 |
  | 0.3248        | 9.43  | 500  | 0.9115          | 0.6415   | 0.4571 | 0.7273          | 0.3333               | 0.8966      | 0.6190 | 0.6149 |
  | 0.3117        | 11.32 | 600  | 0.8608          | 0.6604   | 0.5263 | 0.7143          | 0.4167               | 0.8621      | 0.6410 | 0.6394 |
  | 0.4208        | 13.21 | 700  | 0.8774          | 0.6792   | 0.5641 | 0.7333          | 0.4583               | 0.8621      | 0.6579 | 0.6602 |
  | 0.5267        | 15.09 | 800  | 1.0131          | 0.6792   | 0.5405 | 0.7692          | 0.4167               | 0.8966      | 0.65   | 0.6566 |
  | 0.234         | 16.98 | 900  | 1.1498          | 0.6981   | 0.5556 | 0.8333          | 0.4167               | 0.9310      | 0.6585 | 0.6739 |
  | 0.7581        | 18.87 | 1000 | 1.0952          | 0.7170   | 0.6154 | 0.8             | 0.5                  | 0.8966      | 0.6842 | 0.6983 |
  | 0.1689        | 20.75 | 1100 | 1.1653          | 0.6981   | 0.5789 | 0.7857          | 0.4583               | 0.8966      | 0.6667 | 0.6774 |
  | 0.0765        | 22.64 | 1200 | 1.1245          | 0.7170   | 0.6667 | 0.7143          | 0.625                | 0.7931      | 0.7188 | 0.7091 |
  | 0.6287        | 24.53 | 1300 | 1.2222          | 0.6981   | 0.6    | 0.75            | 0.5                  | 0.8621      | 0.6757 | 0.6810 |
  | 0.0527        | 26.42 | 1400 | 1.2350          | 0.7358   | 0.6818 | 0.75            | 0.625                | 0.8276      | 0.7273 | 0.7263 |
  | 0.3622        | 28.3  | 1500 | 1.1022          | 0.7547   | 0.6667 | 0.8667          | 0.5417               | 0.9310      | 0.7105 | 0.7364 |
  | 0.3227        | 30.19 | 1600 | 1.1541          | 0.7170   | 0.6154 | 0.8             | 0.5                  | 0.8966      | 0.6842 | 0.6983 |
  | 0.3849        | 32.08 | 1700 | 1.2818          | 0.7170   | 0.6154 | 0.8             | 0.5                  | 0.8966      | 0.6842 | 0.6983 |
  | 0.4528        | 33.96 | 1800 | 1.3213          | 0.6981   | 0.5789 | 0.7857          | 0.4583               | 0.8966      | 0.6667 | 0.6774 |
  | 0.1824        | 35.85 | 1900 | 1.3171          | 0.7170   | 0.6512 | 0.7368          | 0.5833               | 0.8276      | 0.7059 | 0.7055 |
  | 0.0367        | 37.74 | 2000 | 1.4484          | 0.7170   | 0.6154 | 0.8             | 0.5                  | 0.8966      | 0.6842 | 0.6983 |
  | 0.07          | 39.62 | 2100 | 1.3521          | 0.7547   | 0.6977 | 0.7895          | 0.625                | 0.8621      | 0.7353 | 0.7435 |
  | 0.0696      