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---
library_name: transformers
base_model: nguyenkhoa/dinov2_Liveness_detection_v2.1.2
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: dinov2_Liveness_detection_v2.1.3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<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/svxcqjbb)
# dinov2_Liveness_detection_v2.1.3
This model is a fine-tuned version of [nguyenkhoa/dinov2_Liveness_detection_v2.1.2](https://huggingface.co/nguyenkhoa/dinov2_Liveness_detection_v2.1.2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0123
- Accuracy: 0.9976
- F1: 0.9976
- Recall: 0.9976
- Precision: 0.9976
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.1276 | 0.3232 | 64 | 0.0239 | 0.992 | 0.9920 | 0.992 | 0.9921 |
| 0.0273 | 0.6465 | 128 | 0.0253 | 0.9908 | 0.9908 | 0.9908 | 0.9908 |
| 0.0236 | 0.9697 | 192 | 0.0257 | 0.9908 | 0.9908 | 0.9908 | 0.9908 |
| 0.015 | 1.2929 | 256 | 0.0223 | 0.9936 | 0.9936 | 0.9936 | 0.9936 |
| 0.0133 | 1.6162 | 320 | 0.0144 | 0.9954 | 0.9954 | 0.9954 | 0.9954 |
| 0.0149 | 1.9394 | 384 | 0.0271 | 0.9913 | 0.9913 | 0.9913 | 0.9914 |
| 0.0097 | 2.2626 | 448 | 0.0234 | 0.9922 | 0.9922 | 0.9922 | 0.9922 |
| 0.009 | 2.5859 | 512 | 0.0149 | 0.9954 | 0.9954 | 0.9954 | 0.9954 |
| 0.0076 | 2.9091 | 576 | 0.0184 | 0.9952 | 0.9952 | 0.9952 | 0.9952 |
| 0.0045 | 3.2323 | 640 | 0.0201 | 0.9951 | 0.9951 | 0.9951 | 0.9951 |
| 0.0032 | 3.5556 | 704 | 0.0169 | 0.9958 | 0.9958 | 0.9958 | 0.9958 |
| 0.0029 | 3.8788 | 768 | 0.0178 | 0.9961 | 0.9960 | 0.9961 | 0.9961 |
| 0.002 | 4.2020 | 832 | 0.0148 | 0.9969 | 0.9969 | 0.9969 | 0.9969 |
| 0.001 | 4.5253 | 896 | 0.0135 | 0.9973 | 0.9973 | 0.9973 | 0.9973 |
| 0.0007 | 4.8485 | 960 | 0.0123 | 0.9976 | 0.9976 | 0.9976 | 0.9976 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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