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---
license: mit
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
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
---
<!-- 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 |