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: MattyB95/VIT-ASVspoof2019-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: 0.9560060081137611
          - name: F1
            type: f1
            value: 0.9749764456013159
          - name: Precision
            type: precision
            value: 0.995013037809648
          - name: Recall
            type: recall
            value: 0.9557308788078018
language:
  - en

VIT-ASVspoof2019-ConstantQ-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: 0.2115
Accuracy: 0.9560
F1: 0.9750
Precision: 0.9950
Recall: 0.9557

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
0.0383	1.0	3173	0.1192	0.9753	0.9864	0.9734	0.9997
0.0158	2.0	6346	0.0505	0.9888	0.9938	0.9911	0.9965
0.0021	3.0	9519	0.1042	0.9849	0.9917	0.9836	0.9998

Framework versions

Transformers 4.36.2
Pytorch 2.1.2+cu121
Datasets 2.15.0
Tokenizers 0.15.0