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hchcsuim
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batch-size16_Celeb-DF-v2_opencv-1FPS_faces-expand0-aligned_unaugmentation

Image Classification
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swin
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  1. README.md +11 -11
  2. model.safetensors +1 -1
  3. runs/Jun21_22-48-17_Lab4/events.out.tfevents.1718981437.Lab4.10156.0 +3 -0
  4. training_args.bin +1 -1
README.md CHANGED
@@ -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: 0.9945566307889799
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  - name: Precision
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  type: precision
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- value: 0.9972944166598344
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  - name: Recall
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  type: recall
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- value: 0.9966815518865992
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  - name: F1
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  type: f1
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- value: 0.996987890088724
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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
@@ -44,12 +44,12 @@ 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: 0.0155
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- - Accuracy: 0.9946
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- - Precision: 0.9973
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- - Recall: 0.9967
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- - F1: 0.9970
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- - Roc Auc: 0.9994
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  ## Model description
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@@ -83,7 +83,7 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Roc Auc |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:-------:|
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- | 0.0468 | 1.0 | 1266 | 0.0155 | 0.9946 | 0.9973 | 0.9967 | 0.9970 | 0.9994 |
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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: 0.9943221092129949
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  - name: Precision
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  type: precision
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+ value: 0.9971713969472951
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  - name: Recall
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  type: recall
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+ value: 0.996544990235842
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  - name: F1
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  type: f1
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+ value: 0.9968580951860554
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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: 0.0169
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+ - Accuracy: 0.9943
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+ - Precision: 0.9972
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+ - Recall: 0.9965
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+ - F1: 0.9969
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+ - Roc Auc: 0.9993
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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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+ | 0.0403 | 1.0 | 1266 | 0.0169 | 0.9943 | 0.9972 | 0.9965 | 0.9969 | 0.9993 |
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  ### Framework versions
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