---
license: mit
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
- LanceaKing/asvspoof2019
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: VIT-ASVspoof2019-MFCC-Synthetic-Voice-Detection
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9804379327000483
    - name: F1
      type: f1
      value: 0.9892177308426143
    - name: Precision
      type: precision
      value: 0.9787514268153481
    - name: Recall
      type: recall
      value: 0.9999102978112666
language:
- en
---

<!-- 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-ASVspoof2019-MFCC-Synthetic-Voice-Detection

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 imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1213
- Accuracy: 0.9804
- F1: 0.9892
- Precision: 0.9788
- Recall: 0.9999

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0283        | 1.0   | 3173 | 0.0958          | 0.9797   | 0.9888 | 0.9782    | 0.9996 |
| 0.0227        | 2.0   | 6346 | 0.0597          | 0.9874   | 0.9930 | 0.9890    | 0.9971 |
| 0.0036        | 3.0   | 9519 | 0.1213          | 0.9804   | 0.9892 | 0.9788    | 0.9999 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0