---
library_name: transformers
license: apache-2.0
base_model: facebook/convnextv2-tiny-1k-224
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: cool-darkness-225
  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. -->

# cool-darkness-225

This model is a fine-tuned version of [facebook/convnextv2-tiny-1k-224](https://huggingface.co/facebook/convnextv2-tiny-1k-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0830
- Accuracy: 0.9783
- Precision: 0.9787
- Recall: 0.9783
- F1: 0.9783
- Roc Auc: 0.9995

## 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: 40

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1     | Roc Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
| 1.7349        | 1.0   | 161  | 0.7668          | 0.8664   | 0.8687    | 0.8664 | 0.8658 | 0.9880  |
| 0.6429        | 2.0   | 322  | 0.2021          | 0.9498   | 0.9506    | 0.9498 | 0.9498 | 0.9979  |
| 0.4363        | 3.0   | 483  | 0.1325          | 0.9601   | 0.9606    | 0.9601 | 0.9600 | 0.9991  |
| 0.3774        | 4.0   | 644  | 0.1117          | 0.966    | 0.9667    | 0.966  | 0.9660 | 0.9992  |
| 0.3413        | 5.0   | 805  | 0.0979          | 0.9694   | 0.9697    | 0.9694 | 0.9693 | 0.9994  |
| 0.3051        | 6.0   | 966  | 0.0824          | 0.9748   | 0.9749    | 0.9748 | 0.9748 | 0.9995  |
| 0.2808        | 7.0   | 1127 | 0.0796          | 0.9742   | 0.9744    | 0.9742 | 0.9742 | 0.9995  |
| 0.2622        | 8.0   | 1288 | 0.0753          | 0.9764   | 0.9765    | 0.9764 | 0.9765 | 0.9995  |
| 0.2512        | 9.0   | 1449 | 0.0769          | 0.9748   | 0.9748    | 0.9748 | 0.9747 | 0.9996  |
| 0.2368        | 10.0  | 1610 | 0.0816          | 0.9777   | 0.9780    | 0.9777 | 0.9777 | 0.9995  |
| 0.2303        | 11.0  | 1771 | 0.0686          | 0.98     | 0.9800    | 0.98   | 0.9800 | 0.9996  |
| 0.2141        | 12.0  | 1932 | 0.0684          | 0.9791   | 0.9792    | 0.9791 | 0.9791 | 0.9996  |
| 0.2086        | 13.0  | 2093 | 0.0766          | 0.9782   | 0.9787    | 0.9782 | 0.9783 | 0.9996  |
| 0.2012        | 14.0  | 2254 | 0.0830          | 0.9783   | 0.9787    | 0.9783 | 0.9783 | 0.9995  |


### Framework versions

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