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hushem_1x_deit_tiny_adamax_001_fold5

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  1. README.md +54 -54
  2. model.safetensors +1 -1
README.md CHANGED
@@ -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: 0.6097560975609756
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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
@@ -32,8 +32,8 @@ should probably proofread and complete it, then remove this comment. -->
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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: 2.5757
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- - Accuracy: 0.6098
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  ## Model description
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@@ -52,7 +52,7 @@ More information needed
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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- - learning_rate: 0.001
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
@@ -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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- | No log | 1.0 | 6 | 1.5806 | 0.2683 |
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- | 1.936 | 2.0 | 12 | 1.3732 | 0.2683 |
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- | 1.936 | 3.0 | 18 | 1.1956 | 0.5122 |
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- | 1.3784 | 4.0 | 24 | 1.3370 | 0.2683 |
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- | 1.3756 | 5.0 | 30 | 1.3182 | 0.4878 |
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- | 1.3756 | 6.0 | 36 | 1.1965 | 0.3902 |
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- | 1.3121 | 7.0 | 42 | 1.1086 | 0.4878 |
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- | 1.3121 | 8.0 | 48 | 1.1916 | 0.4878 |
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- | 1.2758 | 9.0 | 54 | 1.3218 | 0.2683 |
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- | 1.1549 | 10.0 | 60 | 1.1453 | 0.4878 |
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- | 1.1549 | 11.0 | 66 | 1.2471 | 0.4146 |
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- | 1.0642 | 12.0 | 72 | 1.3138 | 0.5366 |
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- | 1.0642 | 13.0 | 78 | 0.9920 | 0.4390 |
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- | 0.92 | 14.0 | 84 | 0.9268 | 0.5854 |
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- | 0.7401 | 15.0 | 90 | 1.0701 | 0.5122 |
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- | 0.7401 | 16.0 | 96 | 0.9883 | 0.5366 |
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- | 0.6495 | 17.0 | 102 | 0.8616 | 0.6098 |
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- | 0.6495 | 18.0 | 108 | 1.1245 | 0.5610 |
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- | 0.475 | 19.0 | 114 | 1.3207 | 0.6585 |
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- | 0.3212 | 20.0 | 120 | 1.5923 | 0.6098 |
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- | 0.3212 | 21.0 | 126 | 2.0857 | 0.5122 |
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- | 0.1553 | 22.0 | 132 | 2.2171 | 0.5122 |
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- | 0.1553 | 23.0 | 138 | 2.5933 | 0.5366 |
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- | 0.1031 | 24.0 | 144 | 2.0291 | 0.5610 |
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- | 0.1068 | 25.0 | 150 | 2.0073 | 0.6098 |
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- | 0.1068 | 26.0 | 156 | 2.5546 | 0.5122 |
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- | 0.0871 | 27.0 | 162 | 2.1934 | 0.5854 |
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- | 0.0871 | 28.0 | 168 | 2.7013 | 0.5610 |
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- | 0.1961 | 29.0 | 174 | 2.9538 | 0.4878 |
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- | 0.0391 | 30.0 | 180 | 2.3781 | 0.6098 |
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- | 0.0391 | 31.0 | 186 | 2.6823 | 0.5610 |
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- | 0.0244 | 32.0 | 192 | 2.3033 | 0.6341 |
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- | 0.0244 | 33.0 | 198 | 2.5112 | 0.6098 |
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- | 0.0164 | 34.0 | 204 | 2.8134 | 0.5122 |
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- | 0.0047 | 35.0 | 210 | 2.7611 | 0.5122 |
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- | 0.0047 | 36.0 | 216 | 2.6509 | 0.5610 |
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- | 0.0008 | 37.0 | 222 | 2.6009 | 0.6098 |
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- | 0.0008 | 38.0 | 228 | 2.5852 | 0.6098 |
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- | 0.0006 | 39.0 | 234 | 2.5782 | 0.6098 |
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- | 0.0005 | 40.0 | 240 | 2.5761 | 0.6098 |
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- | 0.0005 | 41.0 | 246 | 2.5753 | 0.6098 |
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- | 0.0005 | 42.0 | 252 | 2.5757 | 0.6098 |
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- | 0.0005 | 43.0 | 258 | 2.5757 | 0.6098 |
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- | 0.0005 | 44.0 | 264 | 2.5757 | 0.6098 |
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- | 0.0005 | 45.0 | 270 | 2.5757 | 0.6098 |
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- | 0.0005 | 46.0 | 276 | 2.5757 | 0.6098 |
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- | 0.0005 | 47.0 | 282 | 2.5757 | 0.6098 |
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- | 0.0005 | 48.0 | 288 | 2.5757 | 0.6098 |
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- | 0.0005 | 49.0 | 294 | 2.5757 | 0.6098 |
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- | 0.0005 | 50.0 | 300 | 2.5757 | 0.6098 |
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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: 0.7804878048780488
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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 [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: 0.9886
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+ - Accuracy: 0.7805
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  ## Model description
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  ### Training hyperparameters
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  The following hyperparameters were used during training:
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+ - learning_rate: 0.0001
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  - train_batch_size: 32
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  - eval_batch_size: 32
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  - seed: 42
 
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|
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+ | No log | 1.0 | 6 | 1.2270 | 0.3415 |
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+ | 1.4194 | 2.0 | 12 | 1.0630 | 0.5122 |
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+ | 1.4194 | 3.0 | 18 | 0.7493 | 0.7073 |
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+ | 0.7944 | 4.0 | 24 | 0.7294 | 0.7561 |
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+ | 0.3715 | 5.0 | 30 | 0.6953 | 0.6585 |
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+ | 0.3715 | 6.0 | 36 | 0.5928 | 0.8293 |
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+ | 0.1471 | 7.0 | 42 | 0.5485 | 0.8049 |
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+ | 0.1471 | 8.0 | 48 | 0.8515 | 0.6829 |
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+ | 0.0288 | 9.0 | 54 | 0.5381 | 0.8293 |
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+ | 0.0065 | 10.0 | 60 | 0.8647 | 0.7317 |
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+ | 0.0065 | 11.0 | 66 | 0.7563 | 0.7805 |
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+ | 0.0018 | 12.0 | 72 | 0.7678 | 0.8049 |
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+ | 0.0018 | 13.0 | 78 | 0.8017 | 0.8049 |
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+ | 0.0008 | 14.0 | 84 | 0.8475 | 0.7805 |
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+ | 0.0005 | 15.0 | 90 | 0.8926 | 0.7805 |
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+ | 0.0005 | 16.0 | 96 | 0.9216 | 0.7805 |
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+ | 0.0004 | 17.0 | 102 | 0.9424 | 0.7805 |
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+ | 0.0004 | 18.0 | 108 | 0.9465 | 0.7805 |
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+ | 0.0003 | 19.0 | 114 | 0.9461 | 0.7805 |
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+ | 0.0003 | 20.0 | 120 | 0.9448 | 0.7805 |
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+ | 0.0003 | 21.0 | 126 | 0.9474 | 0.7805 |
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+ | 0.0003 | 22.0 | 132 | 0.9525 | 0.7805 |
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+ | 0.0003 | 23.0 | 138 | 0.9551 | 0.7805 |
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+ | 0.0003 | 24.0 | 144 | 0.9581 | 0.7805 |
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+ | 0.0002 | 25.0 | 150 | 0.9626 | 0.7805 |
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+ | 0.0002 | 26.0 | 156 | 0.9650 | 0.7805 |
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+ | 0.0002 | 27.0 | 162 | 0.9711 | 0.7805 |
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+ | 0.0002 | 28.0 | 168 | 0.9713 | 0.7805 |
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+ | 0.0002 | 29.0 | 174 | 0.9730 | 0.7805 |
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+ | 0.0002 | 30.0 | 180 | 0.9754 | 0.7805 |
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+ | 0.0002 | 31.0 | 186 | 0.9786 | 0.7805 |
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+ | 0.0002 | 32.0 | 192 | 0.9820 | 0.7805 |
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+ | 0.0002 | 33.0 | 198 | 0.9835 | 0.7805 |
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+ | 0.0002 | 34.0 | 204 | 0.9850 | 0.7805 |
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+ | 0.0002 | 35.0 | 210 | 0.9850 | 0.7805 |
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+ | 0.0002 | 36.0 | 216 | 0.9860 | 0.7805 |
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+ | 0.0002 | 37.0 | 222 | 0.9866 | 0.7805 |
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+ | 0.0002 | 38.0 | 228 | 0.9873 | 0.7805 |
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+ | 0.0002 | 39.0 | 234 | 0.9879 | 0.7805 |
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+ | 0.0002 | 40.0 | 240 | 0.9883 | 0.7805 |
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+ | 0.0002 | 41.0 | 246 | 0.9886 | 0.7805 |
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+ | 0.0002 | 42.0 | 252 | 0.9886 | 0.7805 |
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+ | 0.0002 | 43.0 | 258 | 0.9886 | 0.7805 |
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+ | 0.0002 | 44.0 | 264 | 0.9886 | 0.7805 |
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+ | 0.0002 | 45.0 | 270 | 0.9886 | 0.7805 |
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+ | 0.0002 | 46.0 | 276 | 0.9886 | 0.7805 |
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+ | 0.0002 | 47.0 | 282 | 0.9886 | 0.7805 |
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+ | 0.0002 | 48.0 | 288 | 0.9886 | 0.7805 |
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+ | 0.0002 | 49.0 | 294 | 0.9886 | 0.7805 |
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+ | 0.0002 | 50.0 | 300 | 0.9886 | 0.7805 |
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  ### Framework versions
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