---
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