---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- seizure-detection
- generated_from_trainer
model-index:
- name: seizure_vit_jlb_231126_ff_raw_combo_multichannel
  results: []
---

<!-- 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. -->

# seizure_vit_jlb_231126_ff_raw_combo_multichannel

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_freq_combo_multichannel dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6323
- Roc Auc: 0.7311

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Roc Auc |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.3219        | 0.17  | 1000  | 0.6337          | 0.7311  |
| 0.3625        | 0.34  | 2000  | 0.6381          | 0.7038  |
| 0.3435        | 0.51  | 3000  | 0.6890          | 0.7010  |
| 0.2538        | 0.68  | 4000  | 0.7449          | 0.6927  |
| 0.2545        | 0.85  | 5000  | 0.7448          | 0.7049  |
| 0.1387        | 1.02  | 6000  | 1.0363          | 0.7127  |
| 0.1765        | 1.19  | 7000  | 0.8541          | 0.7223  |
| 0.1385        | 1.36  | 8000  | 1.0059          | 0.7228  |
| 0.1602        | 1.53  | 9000  | 0.8951          | 0.7070  |
| 0.1158        | 1.7   | 10000 | 1.0356          | 0.7220  |
| 0.0973        | 1.87  | 11000 | 1.0616          | 0.7132  |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.1
- Datasets 2.15.0
- Tokenizers 0.15.0