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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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- f1 |
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- precision |
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- recall |
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model-index: |
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- name: ViT_ASVspoof_DF |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: imagefolder |
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type: imagefolder |
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config: default |
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split: validation |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.8934108527131783 |
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- name: F1 |
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type: f1 |
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value: 0.8431164853649442 |
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- name: Precision |
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type: precision |
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value: 0.7981829517456884 |
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- name: Recall |
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type: recall |
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value: 0.8934108527131783 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<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) |
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# ViT_ASVspoof_DF |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.8822 |
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- Accuracy: 0.8934 |
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- F1: 0.8431 |
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- Precision: 0.7982 |
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- Recall: 0.8934 |
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- Test: 1 |
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- Auc Roc: 0.3976 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0001 |
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- train_batch_size: 128 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 2 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Test | Auc Roc | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:----:|:-------:| |
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| 0.3293 | 0.1078 | 50 | 0.5369 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.4810 | |
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| 0.1251 | 0.2155 | 100 | 0.7074 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5209 | |
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| 0.0671 | 0.3233 | 150 | 0.8683 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5390 | |
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| 0.0463 | 0.4310 | 200 | 0.8867 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5820 | |
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| 0.0365 | 0.5388 | 250 | 0.9675 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.6129 | |
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| 0.0332 | 0.6466 | 300 | 1.1225 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5544 | |
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| 0.0788 | 0.7543 | 350 | 1.1081 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5776 | |
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| 0.0425 | 0.8621 | 400 | 1.4392 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5835 | |
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| 0.0566 | 0.9698 | 450 | 1.8030 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.5043 | |
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| 0.0821 | 1.0776 | 500 | 1.8901 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.6352 | |
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| 0.1122 | 1.1853 | 550 | 1.8085 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.3735 | |
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| 0.0446 | 1.2931 | 600 | 1.9759 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.3383 | |
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| 0.0342 | 1.4009 | 650 | 1.9482 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.4254 | |
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| 0.028 | 1.5086 | 700 | 1.9181 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.3508 | |
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| 0.0195 | 1.6164 | 750 | 1.9146 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.4860 | |
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| 0.0107 | 1.7241 | 800 | 1.8752 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.4285 | |
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| 0.0092 | 1.8319 | 850 | 1.8792 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.4012 | |
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| 0.0 | 1.9397 | 900 | 1.8822 | 0.8934 | 0.8431 | 0.7982 | 0.8934 | 1 | 0.3976 | |
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### Framework versions |
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- Transformers 4.42.3 |
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- Pytorch 2.1.2 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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