metadata

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

VIT-ASVspoof2019-MFCC-Synthetic-Voice-Detection

This model is a fine-tuned version of 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