metadata
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-ASVspoof2019-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: 0.7166781307466625
- name: F1
type: f1
value: 0.8124204206436981
- name: Precision
type: precision
value: 0.9998169964543063
- name: Recall
type: recall
value: 0.6841833380294918
VIT-ASVspoof2019-Mel_Spectrogram-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: 2.0649
- Accuracy: 0.7167
- F1: 0.8124
- Precision: 0.9998
- Recall: 0.6842
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 |
---|---|---|---|---|---|---|---|
0.007 | 1.0 | 3173 | 0.0108 | 0.9972 | 0.9984 | 0.9969 | 1.0 |
0.0015 | 2.0 | 6346 | 0.0022 | 0.9997 | 0.9998 | 0.9999 | 0.9998 |
0.0 | 3.0 | 9519 | 0.0025 | 0.9996 | 0.9998 | 0.9997 | 0.9999 |
Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0