---
library_name: transformers
base_model: nguyenkhoa/dinov2_Liveness_detection_v2.2.2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: dinov2_Liveness_detection_v2.2.3
  results: []
---

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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/441mzmc1)
# dinov2_Liveness_detection_v2.2.3

This model is a fine-tuned version of [nguyenkhoa/dinov2_Liveness_detection_v2.2.2](https://huggingface.co/nguyenkhoa/dinov2_Liveness_detection_v2.2.2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0224
- Accuracy: 0.9932
- F1: 0.9932
- Recall: 0.9932
- Precision: 0.9933

## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.0562        | 0.5872 | 64   | 0.0385          | 0.9860   | 0.9860 | 0.9860 | 0.9860    |
| 0.0328        | 1.1743 | 128  | 0.0350          | 0.9884   | 0.9884 | 0.9884 | 0.9885    |
| 0.0251        | 1.7615 | 192  | 0.0311          | 0.9879   | 0.9879 | 0.9879 | 0.9879    |
| 0.0185        | 2.3486 | 256  | 0.0296          | 0.9895   | 0.9895 | 0.9895 | 0.9895    |
| 0.0166        | 2.9358 | 320  | 0.0328          | 0.9897   | 0.9897 | 0.9897 | 0.9898    |
| 0.0109        | 3.5229 | 384  | 0.0336          | 0.9906   | 0.9906 | 0.9906 | 0.9907    |
| 0.0098        | 4.1101 | 448  | 0.0249          | 0.9917   | 0.9917 | 0.9917 | 0.9917    |
| 0.0069        | 4.6972 | 512  | 0.0224          | 0.9932   | 0.9932 | 0.9932 | 0.9933    |

### Evaluate results 
- APCER: 0.1827
- BPCER: 0.0089
- ACER: 0.0958
- Accuracy: 0.8700
- F1: 0.8975
- Recall: 0.9911
- Precision: 0.7026

### Framework versions

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