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hchcsuim
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batch-size16_FFPP-raw_opencv-1FPS_faces-expand0-aligned_unaugmentation_seeds-42_143_4090

Image Classification
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TensorBoard
Safetensors
swin
Generated from Trainer
Eval Results
Inference Endpoints
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  1. README.md +10 -10
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@@ -25,16 +25,16 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9835503925216681
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  - name: Precision
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  type: precision
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- value: 0.9834235162758739
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  - name: Recall
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  type: recall
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- value: 0.995767625231911
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  - name: F1
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  type: f1
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- value: 0.9895570759812747
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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
@@ -44,11 +44,11 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0446
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- - Accuracy: 0.9836
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- - Precision: 0.9834
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- - Recall: 0.9958
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- - F1: 0.9896
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  - Roc Auc: 0.9989
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
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- | 0.0406 | 0.9996 | 1377 | 0.0446 | 0.9836 | 0.9834 | 0.9958 | 0.9896 | 0.9989 |
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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: 0.9825634160729682
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  - name: Precision
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  type: precision
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+ value: 0.9818833850066437
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  - name: Recall
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  type: recall
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+ value: 0.9961009972170687
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  - name: F1
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  type: f1
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+ value: 0.9889410935150342
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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/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.0478
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+ - Accuracy: 0.9826
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+ - Precision: 0.9819
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+ - Recall: 0.9961
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+ - F1: 0.9889
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  - Roc Auc: 0.9989
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  ## Model description
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
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  |:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
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+ | 0.0428 | 0.9996 | 1377 | 0.0478 | 0.9826 | 0.9819 | 0.9961 | 0.9889 | 0.9989 |
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  ### Framework versions