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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-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
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- name: F1
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type: f1
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value: 0.8263822744093812
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- 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
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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-Mel_Spectrogram-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: 2.0728
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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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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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| 0.0047 | 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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