transformers pytorch vit image-classification generated_from_trainer dataset:arrow model-index autotrain_compatible endpoints_compatible region:us
---
base_model: /content/drive/MyDrive/Seizure_EEG_Research/ViT_Seizure_Detection
tags:
- image-classification
- generated_from_trainer
datasets:
- arrow
metrics:
- matthews_correlation
model-index:
- name: ViT_Seizure_Detection
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: JLB-JLB/seizure_eeg_greyscale_224x224_6secWindow
      type: arrow
      config: default
      split: test
      args: default
    metrics:
    - name: Matthews Correlation
      type: matthews_correlation
      value: 0.41096197273922397
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# ViT_Seizure_Detection

This model is a fine-tuned version of [/content/drive/MyDrive/Seizure_EEG_Research/ViT_Seizure_Detection](https://huggingface.co//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
