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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: facebook/dinov2-small |
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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 |
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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/b39fcrkm) |
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# dinov2_Liveness_detection_v2.2 |
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This model is a fine-tuned version of [facebook/dinov2-small](https://huggingface.co/facebook/dinov2-small) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1307 |
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- Accuracy: 0.9781 |
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- F1: 0.9781 |
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- Recall: 0.9781 |
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- Precision: 0.9783 |
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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: 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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| 0.3279 | 0.2048 | 128 | 0.2858 | 0.8749 | 0.8772 | 0.8749 | 0.8773 | |
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| 0.2389 | 0.4096 | 256 | 0.2696 | 0.8881 | 0.8819 | 0.8881 | 0.9196 | |
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| 0.186 | 0.6144 | 384 | 0.1614 | 0.9383 | 0.9386 | 0.9383 | 0.9381 | |
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| 0.2048 | 0.8192 | 512 | 0.1568 | 0.9404 | 0.9411 | 0.9404 | 0.9415 | |
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| 0.1662 | 1.024 | 640 | 0.1474 | 0.9426 | 0.9433 | 0.9426 | 0.9436 | |
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| 0.1257 | 1.2288 | 768 | 0.1186 | 0.9578 | 0.9573 | 0.9578 | 0.9604 | |
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| 0.1215 | 1.4336 | 896 | 0.1202 | 0.9556 | 0.9560 | 0.9556 | 0.9561 | |
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| 0.0917 | 1.6384 | 1024 | 0.1045 | 0.9611 | 0.9611 | 0.9611 | 0.9611 | |
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| 0.1256 | 1.8432 | 1152 | 0.0971 | 0.9633 | 0.9630 | 0.9633 | 0.9645 | |
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| 0.0676 | 2.048 | 1280 | 0.1524 | 0.9487 | 0.9477 | 0.9487 | 0.9545 | |
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| 0.0458 | 2.2528 | 1408 | 0.1149 | 0.9641 | 0.9643 | 0.9641 | 0.9642 | |
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| 0.0462 | 2.4576 | 1536 | 0.1233 | 0.9630 | 0.9632 | 0.9630 | 0.9631 | |
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| 0.0453 | 2.6624 | 1664 | 0.1030 | 0.9671 | 0.9670 | 0.9671 | 0.9679 | |
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| 0.0631 | 2.8672 | 1792 | 0.0896 | 0.967 | 0.9672 | 0.967 | 0.9671 | |
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| 0.0358 | 3.072 | 1920 | 0.0966 | 0.9735 | 0.9734 | 0.9735 | 0.9738 | |
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| 0.0229 | 3.2768 | 2048 | 0.1250 | 0.9675 | 0.9676 | 0.9675 | 0.9676 | |
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| 0.0272 | 3.4816 | 2176 | 0.1148 | 0.9691 | 0.9693 | 0.9691 | 0.9692 | |
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| 0.0253 | 3.6864 | 2304 | 0.1130 | 0.9757 | 0.9755 | 0.9757 | 0.9761 | |
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| 0.0249 | 3.8912 | 2432 | 0.1091 | 0.9716 | 0.9717 | 0.9716 | 0.9715 | |
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| 0.0049 | 4.096 | 2560 | 0.1420 | 0.9756 | 0.9756 | 0.9756 | 0.9755 | |
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| 0.0159 | 4.3008 | 2688 | 0.1423 | 0.9775 | 0.9774 | 0.9775 | 0.9777 | |
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| 0.0026 | 4.5056 | 2816 | 0.1454 | 0.9774 | 0.9773 | 0.9774 | 0.9776 | |
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| 0.0059 | 4.7104 | 2944 | 0.1445 | 0.9785 | 0.9785 | 0.9785 | 0.9785 | |
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| 0.0011 | 4.9152 | 3072 | 0.1307 | 0.9781 | 0.9781 | 0.9781 | 0.9783 | |
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### Evaluate results |
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- Accuaracy: 0.81 |
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- F1: 0.86 |
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- Recall: 0.85 |
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- Precision: 0.65 |
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- APCER: 0.2001 |
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- BPCER: 0.1458 |
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- ACER: 0.1729 |
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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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