| ---
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| library_name: transformers
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| license: apache-2.0
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| base_model: facebook/convnext-tiny-224
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| tags:
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| - generated_from_trainer
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| metrics:
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| - accuracy
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| - precision
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| - recall
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| - f1
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| model-index:
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| - name: dark-pond-236
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| results: []
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| ---
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|
|
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You
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| should probably proofread and complete it, then remove this comment. -->
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|
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| # dark-pond-236
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|
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| This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on an unknown dataset.
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| It achieves the following results on the evaluation set:
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| - Loss: 0.1304
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| - Accuracy: 0.9611
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| - Precision: 0.9611
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| - Recall: 0.9611
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| - F1: 0.9611
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| - Roc Auc: 0.9990
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|
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| ## Model description
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| More information needed
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|
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| ## Intended uses & limitations
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| More information needed
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|
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| ## Training and evaluation data
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| More information needed
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|
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| ## Training procedure
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| ### Training hyperparameters
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| The following hyperparameters were used during training:
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| - learning_rate: 0.0001
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| - train_batch_size: 256
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| - eval_batch_size: 256
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| - seed: 42
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| - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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| - lr_scheduler_type: cosine
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| - lr_scheduler_warmup_ratio: 0.1
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| - num_epochs: 5
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|
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| ### Training results
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| | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
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| |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
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| | 1.4605 | 1.0 | 161 | 0.4303 | 0.8863 | 0.8910 | 0.8863 | 0.8860 | 0.9934 |
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| | 0.6333 | 2.0 | 322 | 0.2051 | 0.9418 | 0.9423 | 0.9418 | 0.9418 | 0.9980 |
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| | 0.5099 | 3.0 | 483 | 0.1538 | 0.9539 | 0.9540 | 0.9539 | 0.9538 | 0.9987 |
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| | 0.4501 | 4.0 | 644 | 0.1353 | 0.9606 | 0.9605 | 0.9606 | 0.9605 | 0.9989 |
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| | 0.4281 | 5.0 | 805 | 0.1304 | 0.9611 | 0.9611 | 0.9611 | 0.9611 | 0.9990 |
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| ### Framework versions
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| - Transformers 4.52.3
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| - Pytorch 2.7.0+cpu
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| - Datasets 3.6.0
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| - Tokenizers 0.21.0
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| |
