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--- |
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license: apache-2.0 |
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tags: |
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- image-classification |
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- generated_from_trainer |
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datasets: |
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- uta_rldd |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-driver-drowsiness-detection |
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results: |
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- task: |
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name: Image Classification |
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type: image-classification |
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dataset: |
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name: chbh7051/driver-drowsiness-detection |
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type: uta_rldd |
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config: default |
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split: train |
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args: default |
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metrics: |
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- name: Accuracy |
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type: accuracy |
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value: 0.9930477264186396 |
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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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# vit-driver-drowsiness-detection |
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This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the chbh7051/driver-drowsiness-detection dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0159 |
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- Accuracy: 0.9930 |
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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: 0.0002 |
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- train_batch_size: 16 |
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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: 2 |
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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 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:| |
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| 0.1504 | 0.17 | 500 | 0.1178 | 0.9540 | |
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| 0.0581 | 0.33 | 1000 | 0.1022 | 0.9579 | |
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| 0.0415 | 0.5 | 1500 | 0.0877 | 0.9746 | |
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| 0.0487 | 0.67 | 2000 | 0.0650 | 0.9775 | |
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| 0.0555 | 0.84 | 2500 | 0.0537 | 0.9786 | |
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| 0.0279 | 1.0 | 3000 | 0.0472 | 0.9827 | |
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| 0.0139 | 1.17 | 3500 | 0.0452 | 0.9855 | |
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| 0.0282 | 1.34 | 4000 | 0.0358 | 0.9878 | |
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| 0.0077 | 1.5 | 4500 | 0.0397 | 0.9876 | |
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| 0.0143 | 1.67 | 5000 | 0.0159 | 0.9930 | |
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| 0.0439 | 1.84 | 5500 | 0.0162 | 0.9930 | |
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### Framework versions |
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- Transformers 4.27.4 |
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- Pytorch 1.13.0 |
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- Datasets 2.1.0 |
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- Tokenizers 0.13.2 |
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