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update model card README.md

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@@ -21,10 +21,10 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.5882352941176471
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  - name: F1
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  type: f1
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- value: 0.631578947368421
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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
@@ -34,14 +34,14 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 3.7504
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- - Accuracy: 0.5882
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- - F1: 0.6316
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- - Precision (ppv): 0.5455
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- - Recall (sensitivity): 0.75
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- - Specificity: 0.4444
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- - Npv: 0.6667
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- - Auc: 0.5972
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  ## Model description
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@@ -75,22 +75,22 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision (ppv) | Recall (sensitivity) | Specificity | Npv | Auc |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------------:|:--------------------:|:-----------:|:------:|:------:|
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- | 0.7296 | 6.25 | 100 | 0.6515 | 0.5294 | 0.5 | 0.5 | 0.5 | 0.5556 | 0.5556 | 0.5278 |
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- | 0.6136 | 12.49 | 200 | 0.6160 | 0.6471 | 0.5 | 0.75 | 0.375 | 0.8889 | 0.6154 | 0.6319 |
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- | 0.5701 | 18.74 | 300 | 0.6643 | 0.6471 | 0.5714 | 0.6667 | 0.5 | 0.7778 | 0.6364 | 0.6389 |
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- | 0.348 | 24.98 | 400 | 1.3046 | 0.5882 | 0.6316 | 0.5455 | 0.75 | 0.4444 | 0.6667 | 0.5972 |
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- | 0.7343 | 31.25 | 500 | 1.3682 | 0.5882 | 0.6316 | 0.5455 | 0.75 | 0.4444 | 0.6667 | 0.5972 |
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- | 0.4244 | 37.49 | 600 | 2.4365 | 0.5294 | 0.5556 | 0.5 | 0.625 | 0.4444 | 0.5714 | 0.5347 |
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- | 0.4067 | 43.74 | 700 | 2.1054 | 0.5882 | 0.5333 | 0.5714 | 0.5 | 0.6667 | 0.6 | 0.5833 |
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- | 0.446 | 49.98 | 800 | 3.2303 | 0.5294 | 0.5556 | 0.5 | 0.625 | 0.4444 | 0.5714 | 0.5347 |
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- | 0.4791 | 56.25 | 900 | 2.7902 | 0.5294 | 0.5 | 0.5 | 0.5 | 0.5556 | 0.5556 | 0.5278 |
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- | 0.3505 | 62.49 | 1000 | 2.9710 | 0.5882 | 0.5882 | 0.5556 | 0.625 | 0.5556 | 0.625 | 0.5903 |
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- | 0.0057 | 68.74 | 1100 | 4.3480 | 0.5294 | 0.5556 | 0.5 | 0.625 | 0.4444 | 0.5714 | 0.5347 |
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- | 0.3964 | 74.98 | 1200 | 3.3305 | 0.5294 | 0.5 | 0.5 | 0.5 | 0.5556 | 0.5556 | 0.5278 |
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- | 0.0253 | 81.25 | 1300 | 3.1798 | 0.5882 | 0.5882 | 0.5556 | 0.625 | 0.5556 | 0.625 | 0.5903 |
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- | 0.0585 | 87.49 | 1400 | 4.3246 | 0.5294 | 0.5556 | 0.5 | 0.625 | 0.4444 | 0.5714 | 0.5347 |
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- | 0.0917 | 93.74 | 1500 | 3.5914 | 0.5294 | 0.5556 | 0.5 | 0.625 | 0.4444 | 0.5714 | 0.5347 |
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- | 0.1333 | 99.98 | 1600 | 3.7504 | 0.5882 | 0.6316 | 0.5455 | 0.75 | 0.4444 | 0.6667 | 0.5972 |
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  ### Framework versions
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8823529411764706
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  - name: F1
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  type: f1
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+ value: 0.8571428571428571
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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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  This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.8748
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+ - Accuracy: 0.8824
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+ - F1: 0.8571
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+ - Precision (ppv): 0.8571
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+ - Recall (sensitivity): 0.8571
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+ - Specificity: 0.9
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+ - Npv: 0.9
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+ - Auc: 0.8786
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision (ppv) | Recall (sensitivity) | Specificity | Npv | Auc |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------------:|:--------------------:|:-----------:|:------:|:------:|
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+ | 0.6624 | 6.25 | 100 | 0.5548 | 0.8235 | 0.7692 | 0.8333 | 0.7143 | 0.9 | 0.8182 | 0.8071 |
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+ | 0.5201 | 12.49 | 200 | 0.4617 | 0.8824 | 0.8571 | 0.8571 | 0.8571 | 0.9 | 0.9 | 0.8786 |
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+ | 0.5172 | 18.74 | 300 | 0.4249 | 0.8235 | 0.8000 | 0.75 | 0.8571 | 0.8 | 0.8889 | 0.8286 |
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+ | 0.4605 | 24.98 | 400 | 0.3172 | 0.8235 | 0.7692 | 0.8333 | 0.7143 | 0.9 | 0.8182 | 0.8071 |
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+ | 0.4894 | 31.25 | 500 | 0.4466 | 0.8235 | 0.7692 | 0.8333 | 0.7143 | 0.9 | 0.8182 | 0.8071 |
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+ | 0.3694 | 37.49 | 600 | 0.5077 | 0.8235 | 0.7692 | 0.8333 | 0.7143 | 0.9 | 0.8182 | 0.8071 |
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+ | 0.6172 | 43.74 | 700 | 0.5722 | 0.7647 | 0.7143 | 0.7143 | 0.7143 | 0.8 | 0.8 | 0.7571 |
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+ | 0.3671 | 49.98 | 800 | 0.7006 | 0.7647 | 0.6667 | 0.8 | 0.5714 | 0.9 | 0.75 | 0.7357 |
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+ | 0.4109 | 56.25 | 900 | 0.4410 | 0.8235 | 0.7692 | 0.8333 | 0.7143 | 0.9 | 0.8182 | 0.8071 |
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+ | 0.3198 | 62.49 | 1000 | 0.7226 | 0.8235 | 0.7692 | 0.8333 | 0.7143 | 0.9 | 0.8182 | 0.8071 |
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+ | 0.4283 | 68.74 | 1100 | 0.8089 | 0.8235 | 0.7692 | 0.8333 | 0.7143 | 0.9 | 0.8182 | 0.8071 |
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+ | 0.3273 | 74.98 | 1200 | 0.9059 | 0.7647 | 0.6667 | 0.8 | 0.5714 | 0.9 | 0.75 | 0.7357 |
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+ | 0.3237 | 81.25 | 1300 | 0.8520 | 0.8235 | 0.7692 | 0.8333 | 0.7143 | 0.9 | 0.8182 | 0.8071 |
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+ | 0.2014 | 87.49 | 1400 | 0.9183 | 0.7647 | 0.6667 | 0.8 | 0.5714 | 0.9 | 0.75 | 0.7357 |
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+ | 0.3204 | 93.74 | 1500 | 0.6769 | 0.8824 | 0.8571 | 0.8571 | 0.8571 | 0.9 | 0.9 | 0.8786 |
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+ | 0.1786 | 99.98 | 1600 | 0.8748 | 0.8824 | 0.8571 | 0.8571 | 0.8571 | 0.9 | 0.9 | 0.8786 |
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  ### Framework versions