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--- |
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license: apache-2.0 |
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base_model: facebook/convnextv2-base-22k-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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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: convnextv2-base-22k-224-finetuned-tekno24 |
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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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# convnextv2-base-22k-224-finetuned-tekno24 |
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This model is a fine-tuned version of [facebook/convnextv2-base-22k-224](https://huggingface.co/facebook/convnextv2-base-22k-224) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9781 |
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- Accuracy: 0.5748 |
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- F1: 0.5697 |
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- Precision: 0.5822 |
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- Recall: 0.5748 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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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: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 12 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 1.2643 | 0.9951 | 102 | 1.1487 | 0.5207 | 0.4764 | 0.4783 | 0.5207 | |
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| 1.1889 | 2.0 | 205 | 1.1038 | 0.5087 | 0.5191 | 0.5565 | 0.5087 | |
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| 1.215 | 2.9951 | 307 | 1.0810 | 0.4830 | 0.4795 | 0.5589 | 0.4830 | |
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| 1.1062 | 4.0 | 410 | 1.0103 | 0.5620 | 0.5281 | 0.5358 | 0.5620 | |
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| 1.089 | 4.9951 | 512 | 1.0459 | 0.5344 | 0.5440 | 0.5720 | 0.5344 | |
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| 1.0335 | 6.0 | 615 | 0.9781 | 0.5748 | 0.5697 | 0.5822 | 0.5748 | |
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| 1.0139 | 6.9951 | 717 | 0.9905 | 0.5592 | 0.5605 | 0.5625 | 0.5592 | |
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| 0.9047 | 8.0 | 820 | 0.9877 | 0.5629 | 0.5525 | 0.5482 | 0.5629 | |
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| 0.8856 | 8.9951 | 922 | 1.0060 | 0.5565 | 0.5569 | 0.5593 | 0.5565 | |
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| 0.8306 | 10.0 | 1025 | 0.9907 | 0.5666 | 0.5574 | 0.5531 | 0.5666 | |
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| 0.8458 | 10.9951 | 1127 | 1.0135 | 0.5500 | 0.5489 | 0.5506 | 0.5500 | |
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| 0.815 | 11.9415 | 1224 | 1.0185 | 0.5491 | 0.5520 | 0.5558 | 0.5491 | |
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### Framework versions |
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- Transformers 4.42.4 |
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- Pytorch 2.4.0+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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