---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: VIT-ASVspoof5-ConstantQ-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: 0.7589634206678174
    - name: F1
      type: f1
      value: 0.8126223301053855
    - name: Precision
      type: precision
      value: 0.828476821192053
    - name: Recall
      type: recall
      value: 0.7973632566799175
---

<!-- 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-ASVspoof5-ConstantQ-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: 2.1171
- Accuracy: 0.7590
- F1: 0.8126
- Precision: 0.8285
- Recall: 0.7974

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0145        | 1.0   | 22795 | 1.4281          | 0.7289   | 0.8193 | 0.7275    | 0.9377 |
| 0.0084        | 2.0   | 45590 | 1.7308          | 0.7343   | 0.7730 | 0.8787    | 0.6899 |
| 0.0           | 3.0   | 68385 | 2.1171          | 0.7590   | 0.8126 | 0.8285    | 0.7974 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1