| ---
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| library_name: transformers
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| license: apache-2.0
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| base_model: facebook/convnextv2-tiny-1k-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: deft-glitter-211
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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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| # deft-glitter-211
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|
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| 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.
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| It achieves the following results on the evaluation set:
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| - Loss: 1.3537
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| - Accuracy: 0.3164
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| - Precision: 0.4989
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| - Recall: 0.3164
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| - F1: 0.3690
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| - Roc Auc: 0.6235
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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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| | 1.4081 | 1.0 | 17 | 1.3928 | 0.2435 | 0.4923 | 0.2435 | 0.3213 | 0.5510 |
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| | 1.3773 | 2.0 | 34 | 1.3727 | 0.2682 | 0.4966 | 0.2682 | 0.3358 | 0.5862 |
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| | 1.3568 | 3.0 | 51 | 1.3597 | 0.3008 | 0.4996 | 0.3008 | 0.3587 | 0.6121 |
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| | 1.3458 | 4.0 | 68 | 1.3544 | 0.3151 | 0.4995 | 0.3151 | 0.3677 | 0.6220 |
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| | 1.3409 | 5.0 | 85 | 1.3537 | 0.3164 | 0.4989 | 0.3164 | 0.3690 | 0.6235 |
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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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| |
