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---
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-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: 0.686952820148989
- name: F1
type: f1
value: 0.7634000386075542
- name: Precision
type: precision
value: 0.9259586867162704
- name: Recall
type: recall
value: 0.6493942490147424
---
<!-- 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-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: 2.8475
- Accuracy: 0.6870
- F1: 0.7634
- Precision: 0.9260
- Recall: 0.6494
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0335 | 1.0 | 22795 | 1.1422 | 0.7655 | 0.8411 | 0.8892 | 0.7979 |
| 0.0104 | 2.0 | 45590 | 1.9972 | 0.6301 | 0.6979 | 0.9567 | 0.5493 |
| 0.0035 | 3.0 | 68385 | 2.8475 | 0.6870 | 0.7634 | 0.9260 | 0.6494 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cu124
- Datasets 2.20.0
- Tokenizers 0.19.1
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