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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-Mel_Spectrogram-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: 0.7633416105001773
- name: F1
type: f1
value: 0.8263822744093812
- name: Precision
type: precision
value: 0.9621029413546957
- name: Recall
type: recall
value: 0.7242190921033426
---
<!-- 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-Mel_Spectrogram-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: 2.0728
- Accuracy: 0.7633
- F1: 0.8264
- Precision: 0.9621
- Recall: 0.7242
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 0.0047 | 1.0 | 22795 | 0.9664 | 0.8373 | 0.8919 | 0.9221 | 0.8637 |
| 0.0064 | 2.0 | 45590 | 1.6013 | 0.7830 | 0.8421 | 0.9701 | 0.7439 |
| 0.0 | 3.0 | 68385 | 2.0728 | 0.7633 | 0.8264 | 0.9621 | 0.7242 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1
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