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--- |
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library_name: transformers |
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base_model: nguyenkhoa/dinov2_Liveness_detection_v2.1.4 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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- recall |
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- precision |
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model-index: |
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- name: dinov2_Liveness_detection_v2.2.1 |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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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/zdrli5b6) |
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# dinov2_Liveness_detection_v2.2.1 |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0301 |
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- Accuracy: 0.9910 |
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- F1: 0.9910 |
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- Recall: 0.9910 |
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- Precision: 0.9910 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 768 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision | |
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|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:| |
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| 0.4052 | 0.3232 | 64 | 0.0775 | 0.9712 | 0.9713 | 0.9712 | 0.9712 | |
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| 0.0784 | 0.6465 | 128 | 0.0545 | 0.9803 | 0.9803 | 0.9803 | 0.9804 | |
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| 0.0639 | 0.9697 | 192 | 0.0615 | 0.9771 | 0.9772 | 0.9771 | 0.9772 | |
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| 0.0479 | 1.2929 | 256 | 0.0572 | 0.9795 | 0.9794 | 0.9795 | 0.9800 | |
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| 0.0439 | 1.6162 | 320 | 0.0422 | 0.9844 | 0.9844 | 0.9844 | 0.9844 | |
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| 0.0392 | 1.9394 | 384 | 0.0564 | 0.9803 | 0.9801 | 0.9803 | 0.9810 | |
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| 0.0374 | 2.2626 | 448 | 0.0464 | 0.9837 | 0.9837 | 0.9837 | 0.9837 | |
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| 0.0273 | 2.5859 | 512 | 0.0378 | 0.9861 | 0.9861 | 0.9861 | 0.9861 | |
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| 0.0271 | 2.9091 | 576 | 0.0336 | 0.9883 | 0.9883 | 0.9883 | 0.9884 | |
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| 0.021 | 3.2323 | 640 | 0.0418 | 0.9859 | 0.9859 | 0.9859 | 0.9859 | |
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| 0.019 | 3.5556 | 704 | 0.0454 | 0.9848 | 0.9849 | 0.9848 | 0.9849 | |
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| 0.0177 | 3.8788 | 768 | 0.0359 | 0.9883 | 0.9883 | 0.9883 | 0.9883 | |
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| 0.0134 | 4.2020 | 832 | 0.0410 | 0.9874 | 0.9874 | 0.9874 | 0.9877 | |
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| 0.0102 | 4.5253 | 896 | 0.0314 | 0.9910 | 0.9910 | 0.9910 | 0.9910 | |
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| 0.0103 | 4.8485 | 960 | 0.0301 | 0.9910 | 0.9910 | 0.9910 | 0.9910 | |
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### Evaluate results |
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- Accuracy: 0.8626 |
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- F1: 0.8909 |
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- Recall: 0.9924 |
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- Precision: 0.6903 |
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- APCER: 0.1940 |
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- BPCER: 0.0076 |
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- ACER: 0.1008 |
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### Framework versions |
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- Transformers 4.47.0 |
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- Pytorch 2.5.1+cu121 |
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- Datasets 3.2.0 |
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- Tokenizers 0.21.0 |
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