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# 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        | 41.51 | 2200 | 1.2636          | 0.7358   | 0.65   | 0.8125          | 0.5417               | 0.8966      | 0.7027 | 0.7191 |
| 0.1554        | 43.4  | 2300 | 1.2225          | 0.7358   | 0.6667 | 0.7778          | 0.5833               | 0.8621      | 0.7143 | 0.7227 |
| 0.2346        | 45.28 | 2400 | 1.2627          | 0.7547   | 0.6829 | 0.8235          | 0.5833               | 0.8966      | 0.7222 | 0.7399 |
| 0.097         | 47.17 | 2500 | 1.4892          | 0.7170   | 0.6667 | 0.7143          | 0.625                | 0.7931      | 0.7188 | 0.7091 |
| 0.2494        | 49.06 | 2600 | 1.5282          | 0.7170   | 0.6512 | 0.7368          | 0.5833               | 0.8276      | 0.7059 | 0.7055 |
| 0.0734        | 50.94 | 2700 | 1.3989          | 0.7170   | 0.6341 | 0.7647          | 0.5417               | 0.8621      | 0.6944 | 0.7019 |
| 0.1077        | 52.83 | 2800 | 1.5155          | 0.6792   | 0.5641 | 0.7333          | 0.4583               | 0.8621      | 0.6579 | 0.6602 |
| 0.2456        | 54.72 | 2900 | 1.4400          | 0.7170   | 0.6512 | 0.7368          | 0.5833               | 0.8276      | 0.7059 | 0.7055 |
| 0.0823        | 56.6  | 3000 | 1.4511          | 0.7358   | 0.65   | 0.8125          | 0.5417               | 0.8966      | 0.7027 | 0.7191 |
| 0.0471        | 58.49 | 3100 | 1.5114          | 0.7547   | 0.6829 | 0.8235          | 0.5833               | 0.8966      | 0.7222 | 0.7399 |
| 0.0144        | 60.38 | 3200 | 1.4412          | 0.7925   | 0.7317 | 0.8824          | 0.625                | 0.9310      | 0.75   | 0.7780 |
| 0.1235        | 62.26 | 3300 | 1.2029          | 0.7547   | 0.6977 | 0.7895          | 0.625                | 0.8621      | 0.7353 | 0.7435 |
| 0.0121        | 64.15 | 3400 | 1.4925          | 0.7358   | 0.6667 | 0.7778          | 0.5833               | 0.8621      | 0.7143 | 0.7227 |
| 0.2126        | 66.04 | 3500 | 1.3614          | 0.7547   | 0.6667 | 0.8667          | 0.5417               | 0.9310      | 0.7105 | 0.7364 |
| 0.0496        | 67.92 | 3600 | 1.2960          | 0.7736   | 0.7143 | 0.8333          | 0.625                | 0.8966      | 0.7429 | 0.7608 |
| 0.1145        | 69.81 | 3700 | 1.3763          | 0.7547   | 0.6829 | 0.8235          | 0.5833               | 0.8966      | 0.7222 | 0.7399 |
| 0.1272        | 71.7  | 3800 | 1.6328          | 0.7170   | 0.5946 | 0.8462          | 0.4583               | 0.9310      | 0.675  | 0.6947 |
| 0.0007        | 73.58 | 3900 | 1.5622          | 0.7547   | 0.6977 | 0.7895          | 0.625                | 0.8621      | 0.7353 | 0.7435 |
| 0.0101        | 75.47 | 4000 | 1.1811          | 0.7925   | 0.7442 | 0.8421          | 0.6667               | 0.8966      | 0.7647 | 0.7816 |
| 0.0002        | 77.36 | 4100 | 1.8533          | 0.6981   | 0.5789 | 0.7857          | 0.4583               | 0.8966      | 0.6667 | 0.6774 |
| 0.0423        | 79.25 | 4200 | 1.2510          | 0.7547   | 0.6977 | 0.7895          | 0.625                | 0.8621      | 0.7353 | 0.7435 |
| 0.0036        | 81.13 | 4300 | 1.3443          | 0.7547   | 0.6829 | 0.8235          | 0.5833               | 0.8966      | 0.7222 | 0.7399 |
| 0.0432        | 83.02 | 4400 | 1.2864          | 0.7736   | 0.7273 | 0.8             | 0.6667               | 0.8621      | 0.7576 | 0.7644 |
| 0.0021        | 84.91 | 4500 | 0.8999          | 0.7925   | 0.7755 | 0.76            | 0.7917               | 0.7931      | 0.8214 | 0.7924 |
| 0.0002        | 86.79 | 4600 | 1.3634          | 0.7925   | 0.7442 | 0.8421          | 0.6667               | 0.8966      | 0.7647 | 0.7816 |
| 0.0044        | 88.68 | 4700 | 1.7830          | 0.7358   | 0.65   | 0.8125          | 0.5417               | 0.8966      | 0.7027 | 0.7191 |
| 0.0003        | 90.57 | 4800 | 1.2640          | 0.7736   | 0.7273 | 0.8             | 0.6667               | 0.8621      | 0.7576 | 0.7644 |
| 0.0253        | 92.45 | 4900 | 1.2649          | 0.7925   | 0.7442 | 0.8421          | 0.6667               | 0.8966      | 0.7647 | 0.7816 |
| 0.0278        | 94.34 | 5000 | 1.7485          | 0.7170   | 0.6512 | 0.7368          | 0.5833               | 0.8276      | 0.7059 | 0.7055 |
| 0.1608        | 96.23 | 5100 | 1.2641          | 0.8113   | 0.7727 | 0.85            | 0.7083               | 0.8966      | 0.7879 | 0.8024 |
| 0.0017        | 98.11 | 5200 | 1.6380          | 0.7170   | 0.6667 | 0.7143          | 0.625                | 0.7931      | 0.7188 | 0.7091 |
| 0.001         | 100.0 | 5300 | 1.2502          | 0.7925   | 0.7556 | 0.8095          | 0.7083               | 0.8621      | 0.7812 | 0.7852 |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1

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