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

MattyB95/VIT-ASVspoof2019-ConstantQ-Synthetic-Voice-Detection

VIT-ASVspoof2019-ConstantQ-Synthetic-Voice-Detection

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

MattyB95/
VIT-ASVspoof2019-ConstantQ-Synthetic-Voice-Detection