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smids_5x_deit_tiny_adamax_0001_fold2

Image Classification
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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: 0.8885191347753744
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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_fold2
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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: 1.0105
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- - Accuracy: 0.8885
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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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- | 0.2722 | 1.0 | 375 | 0.3276 | 0.8752 |
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- | 0.2367 | 2.0 | 750 | 0.3197 | 0.8819 |
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- | 0.1377 | 3.0 | 1125 | 0.3901 | 0.8636 |
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- | 0.0795 | 4.0 | 1500 | 0.4827 | 0.8752 |
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- | 0.0332 | 5.0 | 1875 | 0.5774 | 0.8769 |
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- | 0.0445 | 6.0 | 2250 | 0.7344 | 0.8785 |
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- | 0.0249 | 7.0 | 2625 | 0.7410 | 0.8802 |
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- | 0.0047 | 8.0 | 3000 | 0.7711 | 0.8819 |
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- | 0.0047 | 9.0 | 3375 | 0.8486 | 0.8769 |
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- | 0.0215 | 10.0 | 3750 | 0.7889 | 0.8835 |
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- | 0.0244 | 11.0 | 4125 | 0.9320 | 0.8802 |
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- | 0.0003 | 12.0 | 4500 | 0.7963 | 0.8985 |
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- | 0.0001 | 13.0 | 4875 | 0.8788 | 0.8802 |
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- | 0.0005 | 14.0 | 5250 | 0.8929 | 0.8852 |
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- | 0.0001 | 15.0 | 5625 | 1.0697 | 0.8719 |
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- | 0.0287 | 16.0 | 6000 | 1.0755 | 0.8652 |
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- | 0.012 | 17.0 | 6375 | 0.9204 | 0.8885 |
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- | 0.0 | 18.0 | 6750 | 0.9345 | 0.8819 |
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- | 0.0 | 19.0 | 7125 | 0.8596 | 0.8835 |
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- | 0.0 | 20.0 | 7500 | 0.9036 | 0.8885 |
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- | 0.0003 | 21.0 | 7875 | 0.9034 | 0.8885 |
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- | 0.0 | 22.0 | 8250 | 0.9984 | 0.8869 |
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- | 0.0022 | 23.0 | 8625 | 0.9293 | 0.8918 |
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- | 0.0 | 24.0 | 9000 | 0.9654 | 0.8885 |
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- | 0.0 | 25.0 | 9375 | 1.0176 | 0.8802 |
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- | 0.0 | 26.0 | 9750 | 1.0066 | 0.8885 |
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- | 0.0 | 27.0 | 10125 | 1.0043 | 0.8918 |
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- | 0.0 | 28.0 | 10500 | 0.9595 | 0.8968 |
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- | 0.0 | 29.0 | 10875 | 0.9919 | 0.8918 |
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- | 0.0 | 30.0 | 11250 | 0.9484 | 0.8952 |
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- | 0.0052 | 31.0 | 11625 | 0.9742 | 0.8952 |
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- | 0.0052 | 32.0 | 12000 | 0.9440 | 0.8902 |
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- | 0.0067 | 33.0 | 12375 | 0.9795 | 0.8968 |
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- | 0.0 | 34.0 | 12750 | 0.9869 | 0.8869 |
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- | 0.0 | 35.0 | 13125 | 0.9947 | 0.8968 |
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- | 0.0 | 36.0 | 13500 | 1.0012 | 0.8869 |
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- | 0.0 | 37.0 | 13875 | 1.0012 | 0.8885 |
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- | 0.0 | 38.0 | 14250 | 1.0023 | 0.8835 |
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- | 0.0 | 39.0 | 14625 | 0.9929 | 0.8935 |
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- | 0.0034 | 40.0 | 15000 | 0.9892 | 0.8935 |
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- | 0.0 | 41.0 | 15375 | 0.9983 | 0.8918 |
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- | 0.0029 | 42.0 | 15750 | 0.9996 | 0.8885 |
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- | 0.0029 | 43.0 | 16125 | 1.0047 | 0.8885 |
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- | 0.0028 | 44.0 | 16500 | 1.0080 | 0.8902 |
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- | 0.0026 | 45.0 | 16875 | 1.0065 | 0.8902 |
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- | 0.0 | 46.0 | 17250 | 1.0050 | 0.8885 |
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- | 0.0053 | 47.0 | 17625 | 1.0096 | 0.8885 |
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- | 0.0 | 48.0 | 18000 | 1.0089 | 0.8885 |
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- | 0.0025 | 49.0 | 18375 | 1.0102 | 0.8885 |
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- | 0.0024 | 50.0 | 18750 | 1.0105 | 0.8885 |
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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: 0.8752079866888519
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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_fold2
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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: 1.2333
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+ - Accuracy: 0.8752
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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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+ | 0.2658 | 1.0 | 375 | 0.3487 | 0.8686 |
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+ | 0.2056 | 2.0 | 750 | 0.3572 | 0.8802 |
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+ | 0.1091 | 3.0 | 1125 | 0.4053 | 0.8785 |
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+ | 0.0803 | 4.0 | 1500 | 0.6864 | 0.8636 |
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+ | 0.0671 | 5.0 | 1875 | 0.6967 | 0.8702 |
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+ | 0.0046 | 6.0 | 2250 | 0.8951 | 0.8636 |
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+ | 0.0027 | 7.0 | 2625 | 0.7926 | 0.8852 |
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+ | 0.0005 | 8.0 | 3000 | 0.7839 | 0.8769 |
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+ | 0.0002 | 9.0 | 3375 | 0.8871 | 0.8869 |
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+ | 0.0002 | 10.0 | 3750 | 0.8009 | 0.8968 |
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+ | 0.0097 | 11.0 | 4125 | 0.9981 | 0.8669 |
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+ | 0.0006 | 12.0 | 4500 | 1.0041 | 0.8719 |
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+ | 0.01 | 13.0 | 4875 | 0.9204 | 0.8735 |
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+ | 0.0001 | 14.0 | 5250 | 0.9628 | 0.8785 |
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+ | 0.036 | 15.0 | 5625 | 0.9459 | 0.8752 |
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+ | 0.0001 | 16.0 | 6000 | 0.9812 | 0.8819 |
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+ | 0.0022 | 17.0 | 6375 | 0.9724 | 0.8819 |
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+ | 0.004 | 18.0 | 6750 | 1.0660 | 0.8769 |
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+ | 0.0 | 19.0 | 7125 | 0.9857 | 0.8719 |
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+ | 0.0 | 20.0 | 7500 | 1.0524 | 0.8752 |
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+ | 0.0 | 21.0 | 7875 | 1.0663 | 0.8686 |
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+ | 0.0 | 22.0 | 8250 | 1.1115 | 0.8686 |
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+ | 0.0 | 23.0 | 8625 | 1.0536 | 0.8752 |
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+ | 0.0 | 24.0 | 9000 | 1.0776 | 0.8719 |
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+ | 0.0 | 25.0 | 9375 | 1.0945 | 0.8686 |
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+ | 0.0 | 26.0 | 9750 | 1.1377 | 0.8802 |
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+ | 0.0 | 27.0 | 10125 | 1.1130 | 0.8735 |
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+ | 0.0 | 28.0 | 10500 | 1.1384 | 0.8735 |
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+ | 0.0 | 29.0 | 10875 | 1.1315 | 0.8769 |
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+ | 0.0 | 30.0 | 11250 | 1.1249 | 0.8752 |
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+ | 0.0037 | 31.0 | 11625 | 1.1445 | 0.8752 |
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+ | 0.0039 | 32.0 | 12000 | 1.1633 | 0.8719 |
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+ | 0.0041 | 33.0 | 12375 | 1.1577 | 0.8785 |
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+ | 0.0 | 34.0 | 12750 | 1.1917 | 0.8686 |
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+ | 0.0 | 35.0 | 13125 | 1.2054 | 0.8702 |
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+ | 0.0 | 36.0 | 13500 | 1.1857 | 0.8735 |
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+ | 0.0 | 37.0 | 13875 | 1.1926 | 0.8719 |
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+ | 0.0 | 38.0 | 14250 | 1.2094 | 0.8719 |
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+ | 0.0 | 39.0 | 14625 | 1.2019 | 0.8769 |
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+ | 0.003 | 40.0 | 15000 | 1.2092 | 0.8752 |
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+ | 0.0 | 41.0 | 15375 | 1.2117 | 0.8769 |
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+ | 0.0026 | 42.0 | 15750 | 1.2195 | 0.8769 |
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+ | 0.0026 | 43.0 | 16125 | 1.2211 | 0.8752 |
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+ | 0.0026 | 44.0 | 16500 | 1.2245 | 0.8769 |
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+ | 0.0024 | 45.0 | 16875 | 1.2259 | 0.8769 |
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+ | 0.0 | 46.0 | 17250 | 1.2293 | 0.8752 |
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+ | 0.0049 | 47.0 | 17625 | 1.2287 | 0.8752 |
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+ | 0.0 | 48.0 | 18000 | 1.2326 | 0.8752 |
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+ | 0.0023 | 49.0 | 18375 | 1.2328 | 0.8752 |
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+ | 0.0024 | 50.0 | 18750 | 1.2333 | 0.8752 |
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  ### Framework versions
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