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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- image-classification |
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- seizure-detection |
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- generated_from_trainer |
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model-index: |
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- name: seizure_vit_jlb_231112_fft_raw_combo |
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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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# seizure_vit_jlb_231112_fft_raw_combo |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the JLB-JLB/seizure_detection_224x224_raw_frequency dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4822 |
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- Roc Auc: 0.7667 |
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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: 2e-06 |
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- train_batch_size: 32 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Roc Auc | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:| |
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| 0.4777 | 0.17 | 500 | 0.5237 | 0.7455 | |
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| 0.4469 | 0.34 | 1000 | 0.5114 | 0.7542 | |
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| 0.4122 | 0.52 | 1500 | 0.5084 | 0.7567 | |
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| 0.3904 | 0.69 | 2000 | 0.5043 | 0.7611 | |
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| 0.3619 | 0.86 | 2500 | 0.5283 | 0.7609 | |
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| 0.3528 | 1.03 | 3000 | 0.5352 | 0.7517 | |
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| 0.3445 | 1.2 | 3500 | 0.5338 | 0.7572 | |
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| 0.3221 | 1.37 | 4000 | 0.5388 | 0.7509 | |
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| 0.3109 | 1.55 | 4500 | 0.5641 | 0.7458 | |
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| 0.3203 | 1.72 | 5000 | 0.5404 | 0.7574 | |
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| 0.294 | 1.89 | 5500 | 0.5421 | 0.7564 | |
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| 0.2964 | 2.06 | 6000 | 0.5582 | 0.7493 | |
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| 0.292 | 2.23 | 6500 | 0.5513 | 0.7561 | |
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| 0.2838 | 2.4 | 7000 | 0.5557 | 0.7598 | |
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| 0.2736 | 2.58 | 7500 | 0.5514 | 0.7606 | |
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| 0.2922 | 2.75 | 8000 | 0.5503 | 0.7538 | |
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| 0.2699 | 2.92 | 8500 | 0.5535 | 0.7578 | |
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### Framework versions |
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- Transformers 4.35.0 |
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- Pytorch 2.1.0 |
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- Datasets 2.14.6 |
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- Tokenizers 0.14.1 |
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