---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: ViT_ASVspoof_DF
  results:
  - task:
      name: Image Classification
      type: image-classification
    dataset:
      name: imagefolder
      type: imagefolder
      config: default
      split: validation
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.8934108527131783
    - name: F1
      type: f1
      value: 0.8431164853649442
    - name: Precision
      type: precision
      value: 0.7981829517456884
    - name: Recall
      type: recall
      value: 0.8934108527131783
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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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)
# ViT_ASVspoof_DF

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.
It achieves the following results on the evaluation set:
- Loss: 1.8822
- Accuracy: 0.8934
- F1: 0.8431
- Precision: 0.7982
- Recall: 0.8934
- Test: 1
- Auc Roc: 0.3976

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 128
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Test | Auc Roc |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:----:|:-------:|
| 0.3293        | 0.1078 | 50   | 0.5369          | 0.8934   | 0.8431 | 0.7982    | 0.8934 | 1    | 0.4810  |
| 0.1251        | 0.2155 | 100  | 0.7074          | 0.8934   | 0.8431 | 0.7982    | 0.8934 | 1    | 0.5209  |
| 0.0671        | 0.3233 | 150  | 0.8683          | 0.8934   | 0.8431 | 0.7982    | 0.8934 | 1    | 0.5390  |
| 0.0463        | 0.4310 | 200  | 0.8867          | 0.8934   | 0.8431 | 0.7982    | 0.8934 | 1    | 0.5820  |
| 0.0365        | 0.5388 | 250  | 0.9675          | 0.8934   | 0.8431 | 0.7982    | 0.8934 | 1    | 0.6129  |
| 0.0332        | 0.6466 | 300  | 1.1225          | 0.8934   | 0.8431 | 0.7982    | 0.8934 | 1    | 0.5544  |
| 0.0788        | 0.7543 | 350  | 1.1081          | 0.8934   | 0.8431 | 0.7982    | 0.8934 | 1    | 0.5776  |
| 0.0425        | 0.8621 | 400  | 1.4392          | 0.8934   | 0.8431 | 0.7982    | 0.8934 | 1    | 0.5835  |
| 0.0566        | 0.9698 | 450  | 1.8030          | 0.8934   | 0.8431 | 0.7982    | 0.8934 | 1    | 0.5043  |
| 0.0821        | 1.0776 | 500  | 1.8901          | 0.8934   | 0.8431 | 0.7982    | 0.8934 | 1    | 0.6352  |
| 0.1122        | 1.1853 | 550  | 1.8085          | 0.8934   | 0.8431 | 0.7982    | 0.8934 | 1    | 0.3735  |
| 0.0446        | 1.2931 | 600  | 1.9759          | 0.8934   | 0.8431 | 0.7982    | 0.8934 | 1    | 0.3383  |
| 0.0342        | 1.4009 | 650  | 1.9482          | 0.8934   | 0.8431 | 0.7982    | 0.8934 | 1    | 0.4254  |
| 0.028         | 1.5086 | 700  | 1.9181          | 0.8934   | 0.8431 | 0.7982    | 0.8934 | 1    | 0.3508  |
| 0.0195        | 1.6164 | 750  | 1.9146          | 0.8934   | 0.8431 | 0.7982    | 0.8934 | 1    | 0.4860  |
| 0.0107        | 1.7241 | 800  | 1.8752          | 0.8934   | 0.8431 | 0.7982    | 0.8934 | 1    | 0.4285  |
| 0.0092        | 1.8319 | 850  | 1.8792          | 0.8934   | 0.8431 | 0.7982    | 0.8934 | 1    | 0.4012  |
| 0.0           | 1.9397 | 900  | 1.8822          | 0.8934   | 0.8431 | 0.7982    | 0.8934 | 1    | 0.3976  |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1