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---
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license: apache-2.0
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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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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-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: 0.686952820148989
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- name: F1
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type: f1
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value: 0.7634000386075542
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- name: Precision
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type: precision
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value: 0.9259586867162704
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- name: Recall
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type: recall
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value: 0.6493942490147424
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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-ASVspoof5-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: 2.8475
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- Accuracy: 0.6870
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- F1: 0.7634
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- Precision: 0.9260
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- Recall: 0.6494
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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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| 0.0335 | 1.0 | 22795 | 1.1422 | 0.7655 | 0.8411 | 0.8892 | 0.7979 |
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| 0.0104 | 2.0 | 45590 | 1.9972 | 0.6301 | 0.6979 | 0.9567 | 0.5493 |
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| 0.0035 | 3.0 | 68385 | 2.8475 | 0.6870 | 0.7634 | 0.9260 | 0.6494 |
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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 2.20.0
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- Tokenizers 0.19.1
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