readme: ---
  license: apache-2.0
  tags:
  - generated_from_trainer
  datasets:
  - imagefolder
  metrics:
  - accuracy
  - f1
  model-index:
  - name: convnext-tiny-224-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: 0.8235294117647058
      - name: F1
        type: f1
        value: 0.7272727272727272
  ---
  
  <!-- 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-brs
  
  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: 0.8667
  - Accuracy: 0.8235
  - F1: 0.7273
  - Precision (ppv): 0.8
  - Recall (sensitivity): 0.6667
  - Specificity: 0.9091
  - Npv: 0.8333
  - Auc: 0.7879
  
  ## 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    |
  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------------:|:--------------------:|:-----------:|:------:|:------:|
  | 0.6766        | 6.25  | 100  | 0.7002          | 0.4706   | 0.5263 | 0.3846          | 0.8333               | 0.2727      | 0.75   | 0.5530 |
  | 0.6408        | 12.49 | 200  | 0.6770          | 0.6471   | 0.5714 | 0.5             | 0.6667               | 0.6364      | 0.7778 | 0.6515 |
  | 0.464         | 18.74 | 300  | 0.6624          | 0.5882   | 0.5882 | 0.4545          | 0.8333               | 0.4545      | 0.8333 | 0.6439 |
  | 0.4295        | 24.98 | 400  | 0.6938          | 0.5294   | 0.5    | 0.4             | 0.6667               | 0.4545      | 0.7143 | 0.5606 |
  | 0.3952        | 31.25 | 500  | 0.5974          | 0.7059   | 0.6154 | 0.5714          | 0.6667               | 0.7273      | 0.8    | 0.6970 |
  | 0.1082        | 37.49 | 600  | 0.6163          | 0.6471   | 0.5    | 0.5             | 0.5                  | 0.7273      | 0.7273 | 0.6136 |
  | 0.1997        | 43.74 | 700  | 0.6155          | 0.7059   | 0.6154 | 0.5714          | 0.6667               | 0.7273      | 0.8    | 0.6970 |
  | 0.1267        | 49.98 | 800  | 0.9063          | 0.6471   | 0.5714 | 0.5             | 0.6667               | 0.6364      | 0.7778 | 0.6515 |
  | 0.1178        | 56.25 | 900  | 0.8672          | 0.7059   | 0.6667 | 0.5556          | 0.8333               | 0.6364      | 0.875  | 0.7348 |
  | 0.2008        | 62.49 | 1000 | 0.7049          | 0.8235   | 0.7692 | 0.7143          | 0.8333               | 0.8182      | 0.9    | 0.8258 |
  | 0.0996        | 68.74 | 1100 | 0.4510          | 0.8235   | 0.7692 | 0.7143          | 0.8333               | 0.8182      | 0.9    | 0.8258 |
  | 0.0115        | 74.98 | 1200 | 0.7561          | 0.8235   | 0.7692 | 0.7143          | 0.8333               | 0.8182      | 0.9    | 0.8258 |
  | 0.0177        | 81.25 | 1300 | 1.0400          | 0.7059   | 0.6667 | 0.5556          | 0.8333               | 0.6364      | 0.875  | 0.7348 |
  | 0.0261        | 87.49 | 1400 | 0.9139          | 0.8235   | 0.7692 | 0.7143          | 0.8333               | 0.8182      | 0.9    | 0.8258 |
  | 0.028         | 93.74 | 1500 | 0.7367          | 0.7647   | 0.7143 | 0.625           | 0.8333               | 0.7273      | 0.8889 | 0.7803 |
  | 0.0056        | 99.98 | 1600 | 0.8667          | 0.8235   | 0.7273 | 0.8             | 0.6667               | 0.9091      | 0.8333 | 0.7879 |
  
  
  ### Framework versions
  
  - Transformers 4.23.1
  - Pytorch 1.12.1+cu113
  - Datasets 2.6.1
  - Tokenizers 0.13.1
  