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