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# dinov2_Liveness_detection_v2.2.1

This model is a fine-tuned version of [nguyenkhoa/dinov2_Liveness_detection_v2.1.4](https://huggingface.co/nguyenkhoa/dinov2_Liveness_detection_v2.1.4) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0301
- Accuracy: 0.9910
- F1: 0.9910
- Recall: 0.9910
- Precision: 0.9910

## 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: 5e-05
- train_batch_size: 768
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.4052        | 0.3232 | 64   | 0.0775          | 0.9712   | 0.9713 | 0.9712 | 0.9712    |
| 0.0784        | 0.6465 | 128  | 0.0545          | 0.9803   | 0.9803 | 0.9803 | 0.9804    |
| 0.0639        | 0.9697 | 192  | 0.0615          | 0.9771   | 0.9772 | 0.9771 | 0.9772    |
| 0.0479        | 1.2929 | 256  | 0.0572          | 0.9795   | 0.9794 | 0.9795 | 0.9800    |
| 0.0439        | 1.6162 | 320  | 0.0422          | 0.9844   | 0.9844 | 0.9844 | 0.9844    |
| 0.0392        | 1.9394 | 384  | 0.0564          | 0.9803   | 0.9801 | 0.9803 | 0.9810    |
| 0.0374        | 2.2626 | 448  | 0.0464          | 0.9837   | 0.9837 | 0.9837 | 0.9837    |
| 0.0273        | 2.5859 | 512  | 0.0378          | 0.9861   | 0.9861 | 0.9861 | 0.9861    |
| 0.0271        | 2.9091 | 576  | 0.0336          | 0.9883   | 0.9883 | 0.9883 | 0.9884    |
| 0.021         | 3.2323 | 640  | 0.0418          | 0.9859   | 0.9859 | 0.9859 | 0.9859    |
| 0.019         | 3.5556 | 704  | 0.0454          | 0.9848   | 0.9849 | 0.9848 | 0.9849    |
| 0.0177        | 3.8788 | 768  | 0.0359          | 0.9883   | 0.9883 | 0.9883 | 0.9883    |
| 0.0134        | 4.2020 | 832  | 0.0410          | 0.9874   | 0.9874 | 0.9874 | 0.9877    |
| 0.0102        | 4.5253 | 896  | 0.0314          | 0.9910   | 0.9910 | 0.9910 | 0.9910    |
| 0.0103        | 4.8485 | 960  | 0.0301          | 0.9910   | 0.9910 | 0.9910 | 0.9910    |

### Evaluate results
- Accuracy: 0.8626
- F1: 0.8909
- Recall: 0.9924
- Precision: 0.6903
- APCER: 0.1940
- BPCER: 0.0076
- ACER: 0.1008


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0

live spoof