sergiocannata
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update model card README.md
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README.md
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metrics:
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- name: Accuracy
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type: accuracy
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value: 0.
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- name: F1
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type: f1
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value: 0.
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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:
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- Accuracy: 0.
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- F1: 0.
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- Precision (ppv): 0.
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- Recall (sensitivity): 0.
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- Specificity: 0.
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- Npv: 0.
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- Auc: 0.
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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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### 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
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