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smids_5x_deit_tiny_adamax_001_fold4

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: 0.8683333333333333
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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_001_fold4
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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: 1.4184
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- - Accuracy: 0.8683
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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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- | 0.3791 | 1.0 | 375 | 0.5149 | 0.81 |
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- | 0.3543 | 2.0 | 750 | 0.3994 | 0.83 |
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- | 0.1787 | 3.0 | 1125 | 0.5603 | 0.8183 |
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- | 0.3211 | 4.0 | 1500 | 0.5360 | 0.8317 |
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- | 0.2723 | 5.0 | 1875 | 0.5513 | 0.8317 |
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- | 0.169 | 6.0 | 2250 | 0.4759 | 0.8533 |
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- | 0.1481 | 7.0 | 2625 | 0.6118 | 0.815 |
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- | 0.0918 | 8.0 | 3000 | 0.6573 | 0.85 |
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- | 0.0777 | 9.0 | 3375 | 0.5688 | 0.8633 |
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- | 0.0593 | 10.0 | 3750 | 0.6144 | 0.8583 |
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- | 0.0781 | 11.0 | 4125 | 0.7021 | 0.8483 |
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- | 0.0801 | 12.0 | 4500 | 0.6706 | 0.855 |
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- | 0.1069 | 13.0 | 4875 | 0.8015 | 0.8467 |
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- | 0.0387 | 14.0 | 5250 | 0.7536 | 0.8567 |
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- | 0.0412 | 15.0 | 5625 | 0.8659 | 0.8533 |
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- | 0.0182 | 16.0 | 6000 | 0.8754 | 0.8633 |
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- | 0.0123 | 17.0 | 6375 | 0.8464 | 0.8617 |
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- | 0.0651 | 18.0 | 6750 | 0.9332 | 0.85 |
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- | 0.0199 | 19.0 | 7125 | 0.9109 | 0.8583 |
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- | 0.0135 | 20.0 | 7500 | 1.0667 | 0.865 |
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- | 0.0006 | 21.0 | 7875 | 1.2062 | 0.8417 |
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- | 0.0032 | 22.0 | 8250 | 1.2094 | 0.8467 |
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- | 0.0103 | 23.0 | 8625 | 1.1431 | 0.8567 |
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- | 0.0009 | 24.0 | 9000 | 1.2426 | 0.8567 |
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- | 0.028 | 25.0 | 9375 | 1.2328 | 0.85 |
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- | 0.0005 | 26.0 | 9750 | 1.2919 | 0.8533 |
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- | 0.0091 | 27.0 | 10125 | 1.2850 | 0.8467 |
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- | 0.0033 | 28.0 | 10500 | 1.2310 | 0.86 |
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- | 0.0 | 29.0 | 10875 | 1.2094 | 0.865 |
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- | 0.0 | 30.0 | 11250 | 1.2953 | 0.8617 |
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- | 0.0063 | 31.0 | 11625 | 1.3319 | 0.8583 |
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- | 0.0181 | 32.0 | 12000 | 1.3122 | 0.8633 |
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- | 0.0 | 33.0 | 12375 | 1.3593 | 0.855 |
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- | 0.0001 | 34.0 | 12750 | 1.3087 | 0.86 |
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- | 0.0 | 35.0 | 13125 | 1.2867 | 0.865 |
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- | 0.0 | 36.0 | 13500 | 1.2867 | 0.8633 |
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- | 0.0 | 37.0 | 13875 | 1.4742 | 0.855 |
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- | 0.0051 | 38.0 | 14250 | 1.3665 | 0.865 |
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- | 0.0 | 39.0 | 14625 | 1.3390 | 0.8633 |
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- | 0.0 | 40.0 | 15000 | 1.3487 | 0.8683 |
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- | 0.0 | 41.0 | 15375 | 1.3566 | 0.8683 |
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- | 0.0 | 42.0 | 15750 | 1.3610 | 0.8683 |
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- | 0.0 | 43.0 | 16125 | 1.3603 | 0.865 |
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- | 0.0 | 44.0 | 16500 | 1.3717 | 0.8667 |
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- | 0.0 | 45.0 | 16875 | 1.3886 | 0.8683 |
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- | 0.0 | 46.0 | 17250 | 1.3960 | 0.8667 |
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- | 0.0 | 47.0 | 17625 | 1.4042 | 0.8667 |
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- | 0.0 | 48.0 | 18000 | 1.4110 | 0.8683 |
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- | 0.0 | 49.0 | 18375 | 1.4162 | 0.8683 |
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- | 0.0 | 50.0 | 18750 | 1.4184 | 0.8683 |
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  ### Framework versions
 
1
  ---
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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: 0.865
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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_001_fold4
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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: 1.5310
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+ - Accuracy: 0.865
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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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+ | 0.4168 | 1.0 | 375 | 0.3631 | 0.85 |
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+ | 0.2785 | 2.0 | 750 | 0.4582 | 0.82 |
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+ | 0.1977 | 3.0 | 1125 | 0.4757 | 0.845 |
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+ | 0.2154 | 4.0 | 1500 | 0.4151 | 0.8567 |
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+ | 0.2216 | 5.0 | 1875 | 0.4921 | 0.84 |
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+ | 0.1277 | 6.0 | 2250 | 0.5208 | 0.84 |
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+ | 0.1577 | 7.0 | 2625 | 0.6509 | 0.84 |
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+ | 0.1043 | 8.0 | 3000 | 0.6131 | 0.8483 |
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+ | 0.0606 | 9.0 | 3375 | 0.7321 | 0.85 |
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+ | 0.0399 | 10.0 | 3750 | 0.7332 | 0.8483 |
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+ | 0.0878 | 11.0 | 4125 | 0.7794 | 0.86 |
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+ | 0.0753 | 12.0 | 4500 | 0.9361 | 0.855 |
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+ | 0.0315 | 13.0 | 4875 | 0.7541 | 0.87 |
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+ | 0.0322 | 14.0 | 5250 | 0.8827 | 0.855 |
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+ | 0.0291 | 15.0 | 5625 | 0.8552 | 0.8667 |
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+ | 0.0323 | 16.0 | 6000 | 1.0097 | 0.8533 |
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+ | 0.0358 | 17.0 | 6375 | 1.0442 | 0.8367 |
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+ | 0.0726 | 18.0 | 6750 | 1.0675 | 0.8533 |
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+ | 0.0105 | 19.0 | 7125 | 1.0350 | 0.8567 |
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+ | 0.0155 | 20.0 | 7500 | 1.0612 | 0.8467 |
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+ | 0.0001 | 21.0 | 7875 | 1.1933 | 0.8467 |
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+ | 0.001 | 22.0 | 8250 | 0.9964 | 0.86 |
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+ | 0.0061 | 23.0 | 8625 | 1.0207 | 0.86 |
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+ | 0.0139 | 24.0 | 9000 | 1.1598 | 0.8467 |
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+ | 0.0232 | 25.0 | 9375 | 1.1652 | 0.8583 |
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+ | 0.0001 | 26.0 | 9750 | 1.1454 | 0.8583 |
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+ | 0.0011 | 27.0 | 10125 | 1.1331 | 0.865 |
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+ | 0.0 | 28.0 | 10500 | 1.2646 | 0.8667 |
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+ | 0.0 | 29.0 | 10875 | 1.1994 | 0.8683 |
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+ | 0.0001 | 30.0 | 11250 | 1.2306 | 0.8533 |
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+ | 0.004 | 31.0 | 11625 | 1.2452 | 0.8617 |
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+ | 0.0 | 32.0 | 12000 | 1.2904 | 0.8633 |
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+ | 0.0 | 33.0 | 12375 | 1.3971 | 0.86 |
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+ | 0.0001 | 34.0 | 12750 | 1.2738 | 0.8633 |
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+ | 0.0 | 35.0 | 13125 | 1.4099 | 0.865 |
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+ | 0.0 | 36.0 | 13500 | 1.3138 | 0.8633 |
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+ | 0.0 | 37.0 | 13875 | 1.3962 | 0.8617 |
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+ | 0.0037 | 38.0 | 14250 | 1.4247 | 0.8633 |
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+ | 0.0 | 39.0 | 14625 | 1.4177 | 0.865 |
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+ | 0.0 | 40.0 | 15000 | 1.4033 | 0.8633 |
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+ | 0.0 | 41.0 | 15375 | 1.4591 | 0.8633 |
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+ | 0.0 | 42.0 | 15750 | 1.4725 | 0.8617 |
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+ | 0.0 | 43.0 | 16125 | 1.4752 | 0.8633 |
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+ | 0.0 | 44.0 | 16500 | 1.4834 | 0.8633 |
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+ | 0.0 | 45.0 | 16875 | 1.4967 | 0.8633 |
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+ | 0.0 | 46.0 | 17250 | 1.5039 | 0.8633 |
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+ | 0.0 | 47.0 | 17625 | 1.5125 | 0.8633 |
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+ | 0.0 | 48.0 | 18000 | 1.5211 | 0.8633 |
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+ | 0.0 | 49.0 | 18375 | 1.5277 | 0.865 |
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+ | 0.0 | 50.0 | 18750 | 1.5310 | 0.865 |
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  ### Framework versions
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