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# avid-sponge-222

This model is a fine-tuned version of [facebook/convnext-base-224](https://huggingface.co/facebook/convnext-base-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1207
- Accuracy: 0.9622
- Precision: 0.9631
- Recall: 0.9622
- F1: 0.9622
- Roc Auc: 0.9955

## 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: 0.0001
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 1.3883        | 1.0   | 17   | 1.3638          | 0.1615   | 0.5315    | 0.1615 | 0.1912 | 0.6131  |
| 1.3166        | 2.0   | 34   | 1.2597          | 0.4622   | 0.7044    | 0.4622 | 0.3777 | 0.7535  |
| 1.1663        | 3.0   | 51   | 1.1127          | 0.4193   | 0.6134    | 0.4193 | 0.4666 | 0.7785  |
| 0.9862        | 4.0   | 68   | 0.8247          | 0.5508   | 0.6135    | 0.5508 | 0.5663 | 0.8250  |
| 0.8022        | 5.0   | 85   | 0.6888          | 0.5417   | 0.6930    | 0.5417 | 0.5632 | 0.8414  |
| 0.6688        | 6.0   | 102  | 0.6198          | 0.5729   | 0.7049    | 0.5729 | 0.5936 | 0.8612  |
| 0.5649        | 7.0   | 119  | 0.5550          | 0.6406   | 0.7094    | 0.6406 | 0.6527 | 0.8871  |
| 0.4652        | 8.0   | 136  | 0.4299          | 0.7253   | 0.7475    | 0.7253 | 0.7265 | 0.9158  |
| 0.3992        | 9.0   | 153  | 0.4714          | 0.7174   | 0.8030    | 0.7174 | 0.7161 | 0.9347  |
| 0.2838        | 10.0  | 170  | 0.3594          | 0.7734   | 0.7821    | 0.7734 | 0.7703 | 0.9419  |
| 0.2476        | 11.0  | 187  | 0.3371          | 0.7747   | 0.8446    | 0.7747 | 0.7716 | 0.9623  |
| 0.1873        | 12.0  | 204  | 0.5076          | 0.7018   | 0.7728    | 0.7018 | 0.7099 | 0.9409  |
| 0.1933        | 13.0  | 221  | 0.2128          | 0.8490   | 0.8705    | 0.8490 | 0.8479 | 0.9800  |
| 0.1069        | 14.0  | 238  | 0.1805          | 0.8971   | 0.9041    | 0.8971 | 0.8980 | 0.9857  |
| 0.0932        | 15.0  | 255  | 0.2421          | 0.8385   | 0.8782    | 0.8385 | 0.8355 | 0.9894  |
| 0.1033        | 16.0  | 272  | 0.1561          | 0.9258   | 0.9307    | 0.9258 | 0.9247 | 0.9936  |
| 0.0343        | 17.0  | 289  | 0.1213          | 0.9531   | 0.9537    | 0.9531 | 0.9531 | 0.9954  |
| 0.0603        | 18.0  | 306  | 0.1270          | 0.9336   | 0.9358    | 0.9336 | 0.9338 | 0.9929  |
| 0.0325        | 19.0  | 323  | 0.0917          | 0.9661   | 0.9672    | 0.9661 | 0.9663 | 0.9975  |
| 0.028         | 20.0  | 340  | 0.1041          | 0.9453   | 0.9492    | 0.9453 | 0.9456 | 0.9968  |
| 0.0283        | 21.0  | 357  | 0.0671          | 0.9674   | 0.9675    | 0.9674 | 0.9674 | 0.9968  |
| 0.0098        | 22.0  | 374  | 0.0663          | 0.9635   | 0.9657    | 0.9635 | 0.9638 | 0.9976  |
| 0.0175        | 23.0  | 391  | 0.0669          | 0.9727   | 0.9730    | 0.9727 | 0.9727 | 0.9973  |
| 0.0106        | 24.0  | 408  | 0.1230          | 0.9622   | 0.9626    | 0.9622 | 0.9623 | 0.9950  |
| 0.0227        | 25.0  | 425  | 0.0757          | 0.9596   | 0.9612    | 0.9596 | 0.9599 | 0.9978  |
| 0.0183        | 26.0  | 442  | 0.1207          | 0.9622   | 0.9631    | 0.9622 | 0.9622 | 0.9955  |


### Framework versions

- Transformers 4.52.3
- Pytorch 2.7.0+cpu
- Datasets 3.6.0
- Tokenizers 0.21.0

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