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smids_5x_deit_tiny_adamax_00001_fold5

Image Classification
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PyTorch
vit
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  1. README.md +55 -55
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -1,6 +1,6 @@
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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: 0.8933333333333333
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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_00001_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: 0.8918
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- - Accuracy: 0.8933
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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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- | 0.3356 | 1.0 | 375 | 0.3637 | 0.8583 |
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- | 0.2941 | 2.0 | 750 | 0.2859 | 0.8917 |
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- | 0.2173 | 3.0 | 1125 | 0.2723 | 0.8933 |
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- | 0.2107 | 4.0 | 1500 | 0.2619 | 0.9017 |
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- | 0.1256 | 5.0 | 1875 | 0.3096 | 0.8767 |
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- | 0.0844 | 6.0 | 2250 | 0.2740 | 0.8983 |
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- | 0.0863 | 7.0 | 2625 | 0.3155 | 0.895 |
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- | 0.0472 | 8.0 | 3000 | 0.3497 | 0.895 |
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- | 0.0763 | 9.0 | 3375 | 0.3686 | 0.895 |
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- | 0.0335 | 10.0 | 3750 | 0.4149 | 0.8967 |
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- | 0.0338 | 11.0 | 4125 | 0.4756 | 0.8933 |
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- | 0.0184 | 12.0 | 4500 | 0.5125 | 0.89 |
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- | 0.0027 | 13.0 | 4875 | 0.6023 | 0.8767 |
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- | 0.0397 | 14.0 | 5250 | 0.6231 | 0.885 |
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- | 0.0014 | 15.0 | 5625 | 0.7069 | 0.8733 |
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- | 0.0003 | 16.0 | 6000 | 0.6770 | 0.8983 |
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- | 0.0258 | 17.0 | 6375 | 0.7038 | 0.895 |
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- | 0.0256 | 18.0 | 6750 | 0.7293 | 0.89 |
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- | 0.0002 | 19.0 | 7125 | 0.7746 | 0.8833 |
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- | 0.0001 | 20.0 | 7500 | 0.7738 | 0.8917 |
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- | 0.0001 | 21.0 | 7875 | 0.8059 | 0.8833 |
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- | 0.026 | 22.0 | 8250 | 0.8287 | 0.8933 |
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- | 0.0138 | 23.0 | 8625 | 0.8293 | 0.8867 |
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- | 0.0 | 24.0 | 9000 | 0.8289 | 0.88 |
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- | 0.0 | 25.0 | 9375 | 0.8428 | 0.8933 |
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- | 0.0001 | 26.0 | 9750 | 0.8343 | 0.8917 |
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- | 0.0016 | 27.0 | 10125 | 0.8455 | 0.89 |
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- | 0.0 | 28.0 | 10500 | 0.8478 | 0.89 |
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- | 0.0001 | 29.0 | 10875 | 0.8508 | 0.8917 |
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- | 0.0173 | 30.0 | 11250 | 0.8741 | 0.8917 |
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- | 0.0 | 31.0 | 11625 | 0.8677 | 0.8933 |
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- | 0.0 | 32.0 | 12000 | 0.8682 | 0.8933 |
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- | 0.0 | 33.0 | 12375 | 0.8819 | 0.8917 |
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- | 0.0 | 34.0 | 12750 | 0.8684 | 0.8917 |
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- | 0.0 | 35.0 | 13125 | 0.8910 | 0.8933 |
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- | 0.0 | 36.0 | 13500 | 0.8845 | 0.8933 |
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- | 0.0 | 37.0 | 13875 | 0.8700 | 0.8917 |
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- | 0.0072 | 38.0 | 14250 | 0.8781 | 0.8917 |
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- | 0.0 | 39.0 | 14625 | 0.8840 | 0.8933 |
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- | 0.0 | 40.0 | 15000 | 0.8993 | 0.895 |
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- | 0.0 | 41.0 | 15375 | 0.8767 | 0.8883 |
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- | 0.0 | 42.0 | 15750 | 0.8820 | 0.8967 |
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- | 0.0 | 43.0 | 16125 | 0.8803 | 0.8983 |
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- | 0.0 | 44.0 | 16500 | 0.8853 | 0.895 |
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- | 0.0 | 45.0 | 16875 | 0.8969 | 0.8917 |
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- | 0.0 | 46.0 | 17250 | 0.8999 | 0.8917 |
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- | 0.0073 | 47.0 | 17625 | 0.8921 | 0.895 |
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- | 0.0 | 48.0 | 18000 | 0.8943 | 0.8917 |
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- | 0.0 | 49.0 | 18375 | 0.8940 | 0.8933 |
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- | 0.0065 | 50.0 | 18750 | 0.8918 | 0.8933 |
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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: 0.8883333333333333
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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_00001_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: 0.8310
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+ - Accuracy: 0.8883
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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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+ | 0.3076 | 1.0 | 375 | 0.3691 | 0.8367 |
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+ | 0.2287 | 2.0 | 750 | 0.2879 | 0.8783 |
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+ | 0.189 | 3.0 | 1125 | 0.2852 | 0.885 |
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+ | 0.1751 | 4.0 | 1500 | 0.2883 | 0.8867 |
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+ | 0.0982 | 5.0 | 1875 | 0.3260 | 0.8833 |
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+ | 0.0575 | 6.0 | 2250 | 0.3348 | 0.875 |
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+ | 0.0552 | 7.0 | 2625 | 0.3820 | 0.8883 |
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+ | 0.0223 | 8.0 | 3000 | 0.4434 | 0.89 |
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+ | 0.0296 | 9.0 | 3375 | 0.4885 | 0.8883 |
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+ | 0.005 | 10.0 | 3750 | 0.5225 | 0.8917 |
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+ | 0.0192 | 11.0 | 4125 | 0.5900 | 0.8883 |
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+ | 0.006 | 12.0 | 4500 | 0.6146 | 0.885 |
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+ | 0.0003 | 13.0 | 4875 | 0.6354 | 0.8867 |
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+ | 0.0012 | 14.0 | 5250 | 0.6738 | 0.8867 |
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+ | 0.0003 | 15.0 | 5625 | 0.7057 | 0.8767 |
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+ | 0.0001 | 16.0 | 6000 | 0.6776 | 0.8967 |
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+ | 0.0001 | 17.0 | 6375 | 0.7281 | 0.885 |
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+ | 0.0151 | 18.0 | 6750 | 0.7671 | 0.88 |
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+ | 0.0 | 19.0 | 7125 | 0.7446 | 0.885 |
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+ | 0.0 | 20.0 | 7500 | 0.7595 | 0.885 |
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+ | 0.0 | 21.0 | 7875 | 0.7847 | 0.8833 |
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+ | 0.0104 | 22.0 | 8250 | 0.8045 | 0.8867 |
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+ | 0.0001 | 23.0 | 8625 | 0.8013 | 0.885 |
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+ | 0.0 | 24.0 | 9000 | 0.8150 | 0.8833 |
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+ | 0.0 | 25.0 | 9375 | 0.8170 | 0.885 |
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+ | 0.0 | 26.0 | 9750 | 0.8095 | 0.8883 |
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+ | 0.0 | 27.0 | 10125 | 0.8047 | 0.8867 |
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+ | 0.0 | 28.0 | 10500 | 0.8115 | 0.8867 |
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+ | 0.0 | 29.0 | 10875 | 0.8193 | 0.8867 |
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+ | 0.0071 | 30.0 | 11250 | 0.8281 | 0.8883 |
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+ | 0.0 | 31.0 | 11625 | 0.8141 | 0.89 |
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+ | 0.0 | 32.0 | 12000 | 0.8187 | 0.89 |
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+ | 0.0 | 33.0 | 12375 | 0.8183 | 0.8883 |
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+ | 0.0 | 34.0 | 12750 | 0.8223 | 0.885 |
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+ | 0.0 | 35.0 | 13125 | 0.8200 | 0.8867 |
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+ | 0.0 | 36.0 | 13500 | 0.8256 | 0.8883 |
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+ | 0.0 | 37.0 | 13875 | 0.8249 | 0.8883 |
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+ | 0.0017 | 38.0 | 14250 | 0.8193 | 0.8883 |
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+ | 0.0 | 39.0 | 14625 | 0.8227 | 0.885 |
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+ | 0.0 | 40.0 | 15000 | 0.8247 | 0.89 |
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+ | 0.0 | 41.0 | 15375 | 0.8288 | 0.885 |
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+ | 0.0 | 42.0 | 15750 | 0.8238 | 0.8917 |
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+ | 0.0 | 43.0 | 16125 | 0.8250 | 0.8883 |
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+ | 0.0 | 44.0 | 16500 | 0.8262 | 0.8917 |
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+ | 0.0 | 45.0 | 16875 | 0.8283 | 0.8917 |
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+ | 0.0 | 46.0 | 17250 | 0.8299 | 0.8867 |
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+ | 0.0027 | 47.0 | 17625 | 0.8304 | 0.8867 |
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+ | 0.0 | 48.0 | 18000 | 0.8306 | 0.8867 |
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+ | 0.0 | 49.0 | 18375 | 0.8310 | 0.8867 |
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+ | 0.004 | 50.0 | 18750 | 0.8310 | 0.8883 |
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  ### Framework versions
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