| ---
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| license: apache-2.0
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| base_model: google/vit-base-patch16-224-in21k
|
| 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-ASVspoof5-Mel_Spectrogram-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: 0.7633416105001773
|
| - name: F1
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| type: f1
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| value: 0.8263822744093812
|
| - name: Precision
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| type: precision
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| value: 0.9621029413546957
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| - name: Recall
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| type: recall
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| value: 0.7242190921033426
|
| ---
|
|
|
| <!-- 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. -->
|
|
|
| # VIT-ASVspoof5-Mel_Spectrogram-Synthetic-Voice-Detection
|
|
|
| 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: 2.0728
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| - Accuracy: 0.7633
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| - F1: 0.8264
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| - Precision: 0.9621
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| - Recall: 0.7242
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|
|
| ## Model description
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|
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| More information needed
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|
|
| ## Intended uses & limitations
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|
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| More information needed
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|
|
| ## Training and evaluation data
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|
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| More information needed
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|
|
| ## Training procedure
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|
|
| ### Training hyperparameters
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|
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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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| | 0.0047 | 1.0 | 22795 | 0.9664 | 0.8373 | 0.8919 | 0.9221 | 0.8637 |
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| | 0.0064 | 2.0 | 45590 | 1.6013 | 0.7830 | 0.8421 | 0.9701 | 0.7439 |
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| | 0.0 | 3.0 | 68385 | 2.0728 | 0.7633 | 0.8264 | 0.9621 | 0.7242 |
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|
|
|
|
| ### Framework versions
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|
|
| - Transformers 4.44.0
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| - Pytorch 2.4.0+cu124
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| - Datasets 2.21.0
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| - Tokenizers 0.19.1
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| |
