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---
library_name: transformers
license: other
base_model: apple/mobilevitv2-1.0-imagenet1k-256
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: mobilevitv2_Liveness_detection_v1.0
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/uhi1thq6)
# mobilevitv2_Liveness_detection_v1.0
This model is a fine-tuned version of [apple/mobilevitv2-1.0-imagenet1k-256](https://huggingface.co/apple/mobilevitv2-1.0-imagenet1k-256) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0046
- Accuracy: 0.9988
- F1: 0.9988
- Recall: 0.9988
- Precision: 0.9988
## 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: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.1093 | 0.2048 | 128 | 0.0679 | 0.9929 | 0.9929 | 0.9929 | 0.9929 |
| 0.0234 | 0.4096 | 256 | 0.0170 | 0.9962 | 0.9962 | 0.9962 | 0.9962 |
| 0.0186 | 0.6144 | 384 | 0.0131 | 0.9973 | 0.9973 | 0.9973 | 0.9973 |
| 0.0068 | 0.8192 | 512 | 0.0089 | 0.9980 | 0.9981 | 0.9980 | 0.9980 |
| 0.0049 | 1.024 | 640 | 0.0067 | 0.9985 | 0.9985 | 0.9985 | 0.9985 |
| 0.0113 | 1.2288 | 768 | 0.0064 | 0.9983 | 0.9984 | 0.9983 | 0.9983 |
| 0.0061 | 1.4336 | 896 | 0.0060 | 0.9983 | 0.9983 | 0.9983 | 0.9984 |
| 0.0025 | 1.6384 | 1024 | 0.0058 | 0.9983 | 0.9983 | 0.9983 | 0.9984 |
| 0.0019 | 1.8432 | 1152 | 0.0053 | 0.9987 | 0.9986 | 0.9987 | 0.9987 |
| 0.0056 | 2.048 | 1280 | 0.0051 | 0.9987 | 0.9987 | 0.9987 | 0.9987 |
| 0.0015 | 2.2528 | 1408 | 0.0050 | 0.9987 | 0.9987 | 0.9987 | 0.9987 |
| 0.0055 | 2.4576 | 1536 | 0.0049 | 0.9988 | 0.9987 | 0.9988 | 0.9988 |
| 0.0023 | 2.6624 | 1664 | 0.0049 | 0.9989 | 0.9988 | 0.9989 | 0.9989 |
| 0.0027 | 2.8672 | 1792 | 0.0046 | 0.9988 | 0.9988 | 0.9988 | 0.9988 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.19.1
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