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--- |
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license: mit |
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base_model: google/vit-base-patch16-224-in21k |
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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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- LanceaKing/asvspoof2019 |
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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-ASVspoof2019-MFCC-Synthetic-Voice-Detection |
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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: 0.9804379327000483 |
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- name: F1 |
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type: f1 |
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value: 0.9892177308426143 |
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- name: Precision |
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type: precision |
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value: 0.9787514268153481 |
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- name: Recall |
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type: recall |
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value: 0.9999102978112666 |
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language: |
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- en |
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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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# VIT-ASVspoof2019-MFCC-Synthetic-Voice-Detection |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1213 |
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- Accuracy: 0.9804 |
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- F1: 0.9892 |
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- Precision: 0.9788 |
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- Recall: 0.9999 |
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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: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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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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- num_epochs: 3.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| |
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| 0.0283 | 1.0 | 3173 | 0.0958 | 0.9797 | 0.9888 | 0.9782 | 0.9996 | |
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| 0.0227 | 2.0 | 6346 | 0.0597 | 0.9874 | 0.9930 | 0.9890 | 0.9971 | |
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| 0.0036 | 3.0 | 9519 | 0.1213 | 0.9804 | 0.9892 | 0.9788 | 0.9999 | |
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### Framework versions |
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- Transformers 4.36.2 |
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- Pytorch 2.1.2+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
