| ---
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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: devout-voice-234
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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
|
| should probably proofread and complete it, then remove this comment. -->
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|
|
| # devout-voice-234
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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: 0.4823
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| - Accuracy: 0.6195
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| - Precision: 0.7580
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| - Recall: 0.6195
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| - F1: 0.6418
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| - Roc Auc: 0.9108
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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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|
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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: 40
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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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| | 1.3843 | 1.0 | 15 | 1.3708 | 0.2922 | 0.5011 | 0.2922 | 0.2957 | 0.6501 |
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| | 1.3354 | 2.0 | 30 | 1.3256 | 0.5062 | 0.5231 | 0.5062 | 0.4827 | 0.7450 |
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| | 1.2134 | 3.0 | 45 | 1.1394 | 0.5508 | 0.5749 | 0.5508 | 0.4751 | 0.7934 |
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| | 1.0408 | 4.0 | 60 | 0.9792 | 0.5188 | 0.6137 | 0.5188 | 0.5357 | 0.8100 |
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| | 0.881 | 5.0 | 75 | 0.6658 | 0.5508 | 0.5913 | 0.5508 | 0.5508 | 0.8320 |
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| | 0.7118 | 6.0 | 90 | 0.6165 | 0.5086 | 0.6760 | 0.5086 | 0.5172 | 0.8318 |
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| | 0.7556 | 7.0 | 105 | 0.5697 | 0.6070 | 0.6564 | 0.6070 | 0.6078 | 0.8671 |
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| | 0.6212 | 8.0 | 120 | 0.5433 | 0.5664 | 0.7000 | 0.5664 | 0.5755 | 0.8680 |
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| | 0.5591 | 9.0 | 135 | 0.4504 | 0.6797 | 0.7197 | 0.6797 | 0.6849 | 0.8983 |
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| | 0.4785 | 10.0 | 150 | 0.4269 | 0.6727 | 0.7115 | 0.6727 | 0.6706 | 0.9120 |
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| | 0.4093 | 11.0 | 165 | 0.4239 | 0.6742 | 0.7948 | 0.6742 | 0.6650 | 0.9345 |
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| | 0.4033 | 12.0 | 180 | 0.4823 | 0.6195 | 0.7580 | 0.6195 | 0.6418 | 0.9108 |
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| ### Framework versions
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|
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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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| |
