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ViT_Seizure_Detection – AI Model by JLB-JLB | AlphaNeural AI
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JLB-JLB
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ViT_Seizure_Detection
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transformers
pytorch
vit
image-classification
generated_from_trainer
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autotrain_compatible
endpoints_compatible
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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
