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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/qnoxkjvk)
# dinov2_Liveness_detection_v2.2.2

This model is a fine-tuned version of [nguyenkhoa/dinov2_Liveness_detection_v2.2.1](https://huggingface.co/nguyenkhoa/dinov2_Liveness_detection_v2.2.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0219
- Accuracy: 0.9934
- F1: 0.9934
- Recall: 0.9934
- Precision: 0.9934

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.069         | 0.3062 | 64   | 0.0579          | 0.9788   | 0.9790 | 0.9788 | 0.9790    |
| 0.0482        | 0.6124 | 128  | 0.0518          | 0.9814   | 0.9815 | 0.9814 | 0.9815    |
| 0.0404        | 0.9187 | 192  | 0.0325          | 0.9882   | 0.9882 | 0.9882 | 0.9882    |
| 0.0325        | 1.2249 | 256  | 0.0407          | 0.9855   | 0.9855 | 0.9855 | 0.9855    |
| 0.0322        | 1.5311 | 320  | 0.0281          | 0.9901   | 0.9901 | 0.9901 | 0.9902    |
| 0.0283        | 1.8373 | 384  | 0.0347          | 0.9884   | 0.9884 | 0.9884 | 0.9885    |
| 0.0256        | 2.1435 | 448  | 0.0271          | 0.9907   | 0.9907 | 0.9907 | 0.9907    |
| 0.0207        | 2.4498 | 512  | 0.0359          | 0.9874   | 0.9874 | 0.9874 | 0.9874    |
| 0.0192        | 2.7560 | 576  | 0.0253          | 0.9917   | 0.9916 | 0.9917 | 0.9917    |
| 0.017         | 3.0622 | 640  | 0.0272          | 0.9908   | 0.9908 | 0.9908 | 0.9908    |
| 0.0134        | 3.3684 | 704  | 0.0255          | 0.9916   | 0.9916 | 0.9916 | 0.9915    |
| 0.0132        | 3.6746 | 768  | 0.0232          | 0.9925   | 0.9925 | 0.9925 | 0.9925    |
| 0.0114        | 3.9809 | 832  | 0.0260          | 0.9919   | 0.9919 | 0.9919 | 0.9918    |
| 0.0074        | 4.2871 | 896  | 0.0242          | 0.9927   | 0.9927 | 0.9927 | 0.9927    |
| 0.0079        | 4.5933 | 960  | 0.0219          | 0.9931   | 0.9931 | 0.9931 | 0.9932    |
| 0.0072        | 4.8995 | 1024 | 0.0219          | 0.9934   | 0.9934 | 0.9934 | 0.9934    |


### Framework versions

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

### Evaluate results
- APCER: 0.1804
- BPCER: 0.0123
- ACER: 0.0963
- Accuracy: 0.8706
- F1: 0.8982
- Recall: 0.9877
- Precision: 0.7046
live spoof