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--- |
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license: apache-2.0 |
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base_model: google/vit-base-patch16-224-in21k |
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tags: |
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- generated_from_trainer |
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datasets: |
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- imagefolder |
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metrics: |
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- accuracy |
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model-index: |
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- name: vit-Covid |
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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: imagefolder |
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type: imagefolder |
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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: 0.9847036328871893 |
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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-Covid |
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This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0805 |
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- Accuracy: 0.9847 |
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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: 10 |
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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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| 0.1283 | 0.38 | 100 | 0.1878 | 0.9484 | |
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| 0.0312 | 0.76 | 200 | 0.1484 | 0.9560 | |
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| 0.0655 | 1.15 | 300 | 0.0976 | 0.9713 | |
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| 0.0587 | 1.53 | 400 | 0.0887 | 0.9713 | |
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| 0.0106 | 1.91 | 500 | 0.0980 | 0.9732 | |
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| 0.0137 | 2.29 | 600 | 0.1479 | 0.9618 | |
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| 0.07 | 2.67 | 700 | 0.0882 | 0.9751 | |
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| 0.0068 | 3.05 | 800 | 0.1160 | 0.9675 | |
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| 0.0321 | 3.44 | 900 | 0.0872 | 0.9694 | |
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| 0.0027 | 3.82 | 1000 | 0.0790 | 0.9809 | |
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| 0.0041 | 4.2 | 1100 | 0.1029 | 0.9713 | |
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| 0.0014 | 4.58 | 1200 | 0.0947 | 0.9809 | |
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| 0.0018 | 4.96 | 1300 | 0.1399 | 0.9713 | |
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| 0.001 | 5.34 | 1400 | 0.0689 | 0.9847 | |
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| 0.001 | 5.73 | 1500 | 0.0852 | 0.9790 | |
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| 0.0008 | 6.11 | 1600 | 0.1111 | 0.9790 | |
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| 0.0013 | 6.49 | 1700 | 0.0695 | 0.9866 | |
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| 0.0049 | 6.87 | 1800 | 0.0728 | 0.9885 | |
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| 0.0007 | 7.25 | 1900 | 0.0963 | 0.9790 | |
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| 0.0012 | 7.63 | 2000 | 0.0886 | 0.9847 | |
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| 0.0006 | 8.02 | 2100 | 0.0811 | 0.9847 | |
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| 0.0015 | 8.4 | 2200 | 0.0796 | 0.9847 | |
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| 0.0143 | 8.78 | 2300 | 0.0804 | 0.9847 | |
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| 0.0005 | 9.16 | 2400 | 0.0816 | 0.9847 | |
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| 0.0006 | 9.54 | 2500 | 0.0811 | 0.9847 | |
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| 0.0005 | 9.92 | 2600 | 0.0805 | 0.9847 | |
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### Framework versions |
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- Transformers 4.38.0.dev0 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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