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library_name: transformers
license: apache-2.0
base_model: facebook/convnext-base-224
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: avid-sponge-222
results: []
---
<!-- 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. -->
# 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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