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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/nguyenkhoaht002/liveness_detection/runs/p2slnhef)
# dinov2_Liveness_detection_v2.1.4

This model is a fine-tuned version of [nguyenkhoa/dinov2_Liveness_detection_v2.1.3](https://huggingface.co/nguyenkhoa/dinov2_Liveness_detection_v2.1.3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0167
- Accuracy: 0.9967
- F1: 0.9967
- Recall: 0.9967
- Precision: 0.9967

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.0402        | 0.5872 | 64   | 0.0219          | 0.9919   | 0.9919 | 0.9919 | 0.9920    |
| 0.0231        | 1.1743 | 128  | 0.0355          | 0.9892   | 0.9891 | 0.9892 | 0.9893    |
| 0.0152        | 1.7615 | 192  | 0.0178          | 0.9935   | 0.9935 | 0.9935 | 0.9935    |
| 0.0085        | 2.3486 | 256  | 0.0279          | 0.9932   | 0.9932 | 0.9932 | 0.9932    |
| 0.0086        | 2.9358 | 320  | 0.0196          | 0.9938   | 0.9938 | 0.9938 | 0.9938    |
| 0.0041        | 3.5229 | 384  | 0.0185          | 0.9957   | 0.9957 | 0.9957 | 0.9957    |
| 0.0018        | 4.1101 | 448  | 0.0191          | 0.9961   | 0.9961 | 0.9961 | 0.9961    |
| 0.0008        | 4.6972 | 512  | 0.0167          | 0.9967   | 0.9967 | 0.9967 | 0.9967    |


### Framework versions

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

### Evaluate results
- APCER: 0.2754
- BPCER: 0.0162
- ACER: 0.1458
- Accuracy: 0.8032
- F1: 0.8369
- Recall: 0.9838
- Precision: 0.6088
live spoof