ViT_Seizure_Detection

JLB-JLB/ViT_Seizure_Detection

ViT_Seizure_Detection is a image classification model from JLB-JLB. Use it when you need a label for an image. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Image Classification
Library: transformers
Model type: vit
Access: Public
Created: Oct 23, 2023
Updated: Oct 26, 2023
SHA: 7c7dd0ba

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Model card

From the Hugging Face model README

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

Task: Image Classification
Library: transformers
Type: vit
Created: Oct 23, 2023
Updated: Oct 26, 2023