ViT_ASVspoof_DF
This model is a fine-tuned version of google/vit-base-patch16-224 on the imagefolder dataset. It achieves the following results on the evaluation set:
- Loss: 1.8822
- Accuracy: 0.8934
- F1: 0.8431
- Precision: 0.7982
- Recall: 0.8934
- Test: 1
- Auc Roc: 0.3976
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: 0.0001
- train_batch_size: 128
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Test | Auc Roc |
|---|---|---|---|---|---|---|---|---|---|
| 0.3293 | 0.1078 | 50 | 0.5369 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.4810 |
| 0.1251 | 0.2155 | 100 | 0.7074 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5209 |
| 0.0671 | 0.3233 | 150 | 0.8683 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5390 |
| 0.0463 | 0.4310 | 200 | 0.8867 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5820 |
| 0.0365 | 0.5388 | 250 | 0.9675 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.6129 |
| 0.0332 | 0.6466 | 300 | 1.1225 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5544 |
| 0.0788 | 0.7543 | 350 | 1.1081 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5776 |
| 0.0425 | 0.8621 | 400 | 1.4392 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5835 |
| 0.0566 | 0.9698 | 450 | 1.8030 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5043 |
| 0.0821 | 1.0776 | 500 | 1.8901 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.6352 |
| 0.1122 | 1.1853 | 550 | 1.8085 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.3735 |
| 0.0446 | 1.2931 | 600 | 1.9759 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.3383 |
| 0.0342 | 1.4009 | 650 | 1.9482 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.4254 |
| 0.028 | 1.5086 | 700 | 1.9181 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.3508 |
| 0.0195 | 1.6164 | 750 | 1.9146 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.4860 |
| 0.0107 | 1.7241 | 800 | 1.8752 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.4285 |
| 0.0092 | 1.8319 | 850 | 1.8792 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.4012 |
| 0.0 | 1.9397 | 900 | 1.8822 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.3976 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1