| --- |
| license: apache-2.0 |
| base_model: google/vit-base-patch16-224 |
| tags: |
| - generated_from_trainer |
| datasets: |
| - imagefolder |
| metrics: |
| - accuracy |
| - f1 |
| - precision |
| - recall |
| model-index: |
| - name: ViT_ASVspoof_DF |
| 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: 0.8934108527131783 |
| - name: F1 |
| type: f1 |
| value: 0.8431164853649442 |
| - name: Precision |
| type: precision |
| value: 0.7981829517456884 |
| - name: Recall |
| type: recall |
| value: 0.8934108527131783 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/bishertello-/uncategorized/runs/q4a21cv3) |
| # ViT_ASVspoof_DF |
|
|
| This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/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 |
| |
