<!-- 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. -->
ViT_Seizure_Detection
This model is a fine-tuned version of /content/drive/MyDrive/Seizure_EEG_Research/ViT_Seizure_Detection on the JLB-JLB/seizure_eeg_greyscale_224x224_6secWindow dataset. It achieves the following results on the evaluation set:
- Loss: 0.1622
- Matthews Correlation: 0.4110
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|---|---|---|---|---|
| 0.0742 | 0.79 | 10000 | 0.2080 | 0.4431 |
| 0.0409 | 1.57 | 20000 | 0.2175 | 0.4470 |
| 0.0345 | 2.36 | 30000 | 0.2514 | 0.4717 |
| 0.0184 | 3.14 | 40000 | 0.3040 | 0.4261 |
| 0.0092 | 3.93 | 50000 | 0.3495 | 0.4389 |
Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
