---
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: []
---

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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/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