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smids_5x_deit_tiny_adamax_0001_fold5

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vit
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  1. README.md +55 -55
  2. pytorch_model.bin +1 -1
README.md CHANGED
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  ---
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  license: apache-2.0
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- base_model: facebook/deit-tiny-patch16-224
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  tags:
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  - generated_from_trainer
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  datasets:
@@ -22,7 +22,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.9
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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
@@ -30,10 +30,10 @@ should probably proofread and complete it, then remove this comment. -->
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  # smids_5x_deit_tiny_adamax_0001_fold5
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- This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.9841
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- - Accuracy: 0.9
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  ## Model description
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@@ -65,56 +65,56 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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- | 0.2507 | 1.0 | 375 | 0.2975 | 0.8817 |
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- | 0.1596 | 2.0 | 750 | 0.2719 | 0.8983 |
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- | 0.15 | 3.0 | 1125 | 0.3446 | 0.8933 |
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- | 0.0735 | 4.0 | 1500 | 0.3936 | 0.8983 |
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- | 0.0478 | 5.0 | 1875 | 0.4642 | 0.8967 |
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- | 0.0406 | 6.0 | 2250 | 0.6513 | 0.8817 |
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- | 0.0121 | 7.0 | 2625 | 0.6790 | 0.8917 |
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- | 0.001 | 8.0 | 3000 | 0.8659 | 0.8817 |
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- | 0.0212 | 9.0 | 3375 | 0.8430 | 0.8867 |
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- | 0.0295 | 10.0 | 3750 | 0.6872 | 0.9067 |
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- | 0.0277 | 11.0 | 4125 | 0.8918 | 0.8783 |
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- | 0.0002 | 12.0 | 4500 | 0.7730 | 0.9017 |
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- | 0.002 | 13.0 | 4875 | 0.8076 | 0.8967 |
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- | 0.0001 | 14.0 | 5250 | 0.8319 | 0.8967 |
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- | 0.0011 | 15.0 | 5625 | 0.8001 | 0.9017 |
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- | 0.0001 | 16.0 | 6000 | 0.8286 | 0.8967 |
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- | 0.0186 | 17.0 | 6375 | 0.8434 | 0.8967 |
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- | 0.0159 | 18.0 | 6750 | 0.8463 | 0.9017 |
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- | 0.0 | 19.0 | 7125 | 0.9420 | 0.8883 |
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- | 0.0226 | 20.0 | 7500 | 0.9009 | 0.8933 |
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- | 0.0032 | 21.0 | 7875 | 0.9301 | 0.885 |
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- | 0.0152 | 22.0 | 8250 | 0.8842 | 0.895 |
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- | 0.0 | 23.0 | 8625 | 0.8902 | 0.8933 |
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- | 0.0 | 24.0 | 9000 | 0.8549 | 0.9 |
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- | 0.0 | 25.0 | 9375 | 0.8775 | 0.9067 |
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- | 0.0 | 26.0 | 9750 | 0.9516 | 0.8917 |
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- | 0.0001 | 27.0 | 10125 | 0.9996 | 0.8917 |
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- | 0.0 | 28.0 | 10500 | 0.8829 | 0.9033 |
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- | 0.0 | 29.0 | 10875 | 0.9076 | 0.8967 |
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- | 0.0079 | 30.0 | 11250 | 1.0004 | 0.8933 |
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- | 0.0 | 31.0 | 11625 | 0.9798 | 0.89 |
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- | 0.0 | 32.0 | 12000 | 0.9959 | 0.8917 |
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- | 0.0 | 33.0 | 12375 | 0.9424 | 0.8967 |
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- | 0.0 | 34.0 | 12750 | 0.9569 | 0.8917 |
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- | 0.0 | 35.0 | 13125 | 0.9484 | 0.9 |
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- | 0.0 | 36.0 | 13500 | 0.9710 | 0.8933 |
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- | 0.0 | 37.0 | 13875 | 0.9404 | 0.9 |
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- | 0.004 | 38.0 | 14250 | 0.9709 | 0.8967 |
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- | 0.0 | 39.0 | 14625 | 0.9503 | 0.8983 |
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- | 0.0 | 40.0 | 15000 | 0.9698 | 0.8983 |
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- | 0.0 | 41.0 | 15375 | 0.9492 | 0.9 |
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- | 0.0 | 42.0 | 15750 | 0.9730 | 0.8983 |
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- | 0.0 | 43.0 | 16125 | 0.9737 | 0.8983 |
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- | 0.0 | 44.0 | 16500 | 0.9781 | 0.8983 |
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- | 0.0 | 45.0 | 16875 | 0.9776 | 0.8983 |
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- | 0.0 | 46.0 | 17250 | 0.9813 | 0.8983 |
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- | 0.0028 | 47.0 | 17625 | 0.9816 | 0.8983 |
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- | 0.0 | 48.0 | 18000 | 0.9854 | 0.8967 |
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- | 0.0 | 49.0 | 18375 | 0.9869 | 0.8967 |
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- | 0.002 | 50.0 | 18750 | 0.9841 | 0.9 |
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  ### Framework versions
 
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  ---
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  license: apache-2.0
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+ base_model: facebook/deit-small-patch16-224
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  tags:
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  - generated_from_trainer
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  datasets:
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.9066666666666666
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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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  # smids_5x_deit_tiny_adamax_0001_fold5
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+ This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.9117
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+ - Accuracy: 0.9067
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:-----:|:---------------:|:--------:|
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+ | 0.1964 | 1.0 | 375 | 0.3187 | 0.86 |
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+ | 0.1451 | 2.0 | 750 | 0.2705 | 0.905 |
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+ | 0.0998 | 3.0 | 1125 | 0.3251 | 0.905 |
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+ | 0.0219 | 4.0 | 1500 | 0.4834 | 0.89 |
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+ | 0.034 | 5.0 | 1875 | 0.5396 | 0.8967 |
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+ | 0.0222 | 6.0 | 2250 | 0.4814 | 0.9133 |
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+ | 0.0006 | 7.0 | 2625 | 0.6569 | 0.9133 |
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+ | 0.013 | 8.0 | 3000 | 0.5398 | 0.91 |
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+ | 0.001 | 9.0 | 3375 | 0.6798 | 0.9117 |
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+ | 0.0003 | 10.0 | 3750 | 0.7290 | 0.9083 |
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+ | 0.0012 | 11.0 | 4125 | 0.7598 | 0.9067 |
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+ | 0.0 | 12.0 | 4500 | 0.6815 | 0.9167 |
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+ | 0.0001 | 13.0 | 4875 | 0.7765 | 0.895 |
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+ | 0.0004 | 14.0 | 5250 | 0.6391 | 0.9117 |
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+ | 0.0001 | 15.0 | 5625 | 0.8172 | 0.9067 |
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+ | 0.003 | 16.0 | 6000 | 0.6833 | 0.9083 |
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+ | 0.0 | 17.0 | 6375 | 0.7002 | 0.9133 |
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+ | 0.0103 | 18.0 | 6750 | 0.7679 | 0.91 |
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+ | 0.0 | 19.0 | 7125 | 0.8144 | 0.905 |
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+ | 0.0 | 20.0 | 7500 | 0.8111 | 0.905 |
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+ | 0.0 | 21.0 | 7875 | 0.8619 | 0.9 |
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+ | 0.0063 | 22.0 | 8250 | 0.7672 | 0.9117 |
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+ | 0.0001 | 23.0 | 8625 | 0.8118 | 0.905 |
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+ | 0.0 | 24.0 | 9000 | 0.7951 | 0.9133 |
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+ | 0.0 | 25.0 | 9375 | 0.8220 | 0.9033 |
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+ | 0.0 | 26.0 | 9750 | 0.7685 | 0.915 |
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+ | 0.0 | 27.0 | 10125 | 0.8895 | 0.9 |
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+ | 0.0 | 28.0 | 10500 | 0.7796 | 0.9167 |
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+ | 0.0 | 29.0 | 10875 | 0.8774 | 0.9083 |
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+ | 0.0031 | 30.0 | 11250 | 0.8526 | 0.905 |
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+ | 0.0 | 31.0 | 11625 | 0.9000 | 0.9 |
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+ | 0.0 | 32.0 | 12000 | 0.8541 | 0.9067 |
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+ | 0.0 | 33.0 | 12375 | 0.8916 | 0.905 |
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+ | 0.0 | 34.0 | 12750 | 0.8844 | 0.905 |
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+ | 0.0 | 35.0 | 13125 | 0.8624 | 0.9067 |
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+ | 0.0 | 36.0 | 13500 | 0.8971 | 0.9017 |
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+ | 0.0 | 37.0 | 13875 | 0.8894 | 0.9083 |
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+ | 0.003 | 38.0 | 14250 | 0.8778 | 0.905 |
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+ | 0.0 | 39.0 | 14625 | 0.8771 | 0.9083 |
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+ | 0.0 | 40.0 | 15000 | 0.8911 | 0.905 |
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+ | 0.0 | 41.0 | 15375 | 0.8755 | 0.91 |
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+ | 0.0 | 42.0 | 15750 | 0.8923 | 0.905 |
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+ | 0.0 | 43.0 | 16125 | 0.8874 | 0.9067 |
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+ | 0.0 | 44.0 | 16500 | 0.8938 | 0.9067 |
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+ | 0.0 | 45.0 | 16875 | 0.8983 | 0.9067 |
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+ | 0.0 | 46.0 | 17250 | 0.9065 | 0.9067 |
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+ | 0.0027 | 47.0 | 17625 | 0.9068 | 0.9067 |
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+ | 0.0 | 48.0 | 18000 | 0.9127 | 0.905 |
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+ | 0.0 | 49.0 | 18375 | 0.9142 | 0.905 |
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+ | 0.0023 | 50.0 | 18750 | 0.9117 | 0.9067 |
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  ### Framework versions
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