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hkivancoral
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smids_3x_beit_base_adamax_00001_fold4

Image Classification
Transformers
PyTorch
beit
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Community
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End of training

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  1. README.md +53 -53
  2. pytorch_model.bin +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: 0.8783333333333333
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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 [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.0288
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- - Accuracy: 0.8783
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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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- | 0.3467 | 1.0 | 225 | 0.3638 | 0.8533 |
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- | 0.172 | 2.0 | 450 | 0.3586 | 0.8667 |
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- | 0.1351 | 3.0 | 675 | 0.3727 | 0.8667 |
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- | 0.1269 | 4.0 | 900 | 0.3879 | 0.8733 |
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- | 0.1062 | 5.0 | 1125 | 0.4147 | 0.88 |
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- | 0.0579 | 6.0 | 1350 | 0.4713 | 0.875 |
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- | 0.0459 | 7.0 | 1575 | 0.5338 | 0.8633 |
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- | 0.0952 | 8.0 | 1800 | 0.5841 | 0.88 |
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- | 0.0784 | 9.0 | 2025 | 0.6416 | 0.8733 |
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- | 0.0154 | 10.0 | 2250 | 0.6838 | 0.87 |
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- | 0.0358 | 11.0 | 2475 | 0.7495 | 0.8783 |
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- | 0.0306 | 12.0 | 2700 | 0.7507 | 0.8717 |
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- | 0.0239 | 13.0 | 2925 | 0.7578 | 0.8867 |
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- | 0.0164 | 14.0 | 3150 | 0.7902 | 0.875 |
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- | 0.0292 | 15.0 | 3375 | 0.8444 | 0.8783 |
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- | 0.0065 | 16.0 | 3600 | 0.7912 | 0.8783 |
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- | 0.0083 | 17.0 | 3825 | 0.8375 | 0.8833 |
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- | 0.0226 | 18.0 | 4050 | 0.8981 | 0.875 |
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- | 0.0156 | 19.0 | 4275 | 0.8780 | 0.8783 |
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- | 0.01 | 20.0 | 4500 | 0.8605 | 0.885 |
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- | 0.0014 | 21.0 | 4725 | 0.9024 | 0.8783 |
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- | 0.0009 | 22.0 | 4950 | 0.9009 | 0.8867 |
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- | 0.0031 | 23.0 | 5175 | 0.8992 | 0.885 |
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- | 0.0245 | 24.0 | 5400 | 0.8932 | 0.8783 |
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- | 0.0046 | 25.0 | 5625 | 0.9584 | 0.875 |
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- | 0.0004 | 26.0 | 5850 | 0.9308 | 0.88 |
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- | 0.0062 | 27.0 | 6075 | 0.9236 | 0.8867 |
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- | 0.0002 | 28.0 | 6300 | 0.9476 | 0.8683 |
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- | 0.0019 | 29.0 | 6525 | 0.9575 | 0.8833 |
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- | 0.0205 | 30.0 | 6750 | 0.9568 | 0.8833 |
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- | 0.0063 | 31.0 | 6975 | 0.9738 | 0.8767 |
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- | 0.0189 | 32.0 | 7200 | 0.9699 | 0.8733 |
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- | 0.0206 | 33.0 | 7425 | 0.9759 | 0.8733 |
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- | 0.0001 | 34.0 | 7650 | 0.9831 | 0.88 |
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- | 0.0097 | 35.0 | 7875 | 0.9933 | 0.88 |
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- | 0.0015 | 36.0 | 8100 | 0.9925 | 0.875 |
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- | 0.0068 | 37.0 | 8325 | 1.0317 | 0.88 |
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- | 0.0061 | 38.0 | 8550 | 0.9900 | 0.8717 |
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- | 0.0235 | 39.0 | 8775 | 0.9977 | 0.8783 |
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- | 0.0407 | 40.0 | 9000 | 1.0330 | 0.8717 |
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- | 0.0054 | 41.0 | 9225 | 1.0564 | 0.8767 |
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- | 0.0099 | 42.0 | 9450 | 1.0338 | 0.8733 |
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- | 0.0073 | 43.0 | 9675 | 1.0313 | 0.8817 |
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- | 0.0023 | 44.0 | 9900 | 1.0262 | 0.875 |
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- | 0.0001 | 45.0 | 10125 | 1.0273 | 0.8767 |
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- | 0.0 | 46.0 | 10350 | 1.0290 | 0.8817 |
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- | 0.0002 | 47.0 | 10575 | 1.0379 | 0.8733 |
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- | 0.0002 | 48.0 | 10800 | 1.0366 | 0.8733 |
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- | 0.0005 | 49.0 | 11025 | 1.0284 | 0.8783 |
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- | 0.0001 | 50.0 | 11250 | 1.0288 | 0.8783 |
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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: 0.8883333333333333
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  ---
27
 
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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 [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.0796
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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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+ | 0.2789 | 1.0 | 375 | 0.3658 | 0.8517 |
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+ | 0.2051 | 2.0 | 750 | 0.3746 | 0.855 |
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+ | 0.0849 | 3.0 | 1125 | 0.3986 | 0.87 |
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+ | 0.1357 | 4.0 | 1500 | 0.4367 | 0.8633 |
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+ | 0.0524 | 5.0 | 1875 | 0.4518 | 0.8867 |
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+ | 0.0622 | 6.0 | 2250 | 0.5510 | 0.8867 |
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+ | 0.0413 | 7.0 | 2625 | 0.6374 | 0.8767 |
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+ | 0.0498 | 8.0 | 3000 | 0.6614 | 0.88 |
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+ | 0.0181 | 9.0 | 3375 | 0.6937 | 0.8867 |
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+ | 0.013 | 10.0 | 3750 | 0.7663 | 0.8817 |
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+ | 0.0051 | 11.0 | 4125 | 0.8001 | 0.8783 |
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+ | 0.0188 | 12.0 | 4500 | 0.8577 | 0.8817 |
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+ | 0.0009 | 13.0 | 4875 | 0.8978 | 0.8783 |
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+ | 0.0123 | 14.0 | 5250 | 0.9022 | 0.885 |
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+ | 0.0631 | 15.0 | 5625 | 0.9459 | 0.875 |
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+ | 0.0113 | 16.0 | 6000 | 0.9699 | 0.8717 |
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+ | 0.0335 | 17.0 | 6375 | 0.9476 | 0.8867 |
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+ | 0.014 | 18.0 | 6750 | 0.9219 | 0.8833 |
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+ | 0.0214 | 19.0 | 7125 | 0.9440 | 0.8833 |
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+ | 0.0134 | 20.0 | 7500 | 0.9965 | 0.8883 |
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+ | 0.0053 | 21.0 | 7875 | 0.9823 | 0.8883 |
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+ | 0.0026 | 22.0 | 8250 | 1.0367 | 0.8817 |
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+ | 0.0174 | 23.0 | 8625 | 1.0171 | 0.8833 |
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+ | 0.0002 | 24.0 | 9000 | 1.0087 | 0.8817 |
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+ | 0.032 | 25.0 | 9375 | 1.0135 | 0.8917 |
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+ | 0.0014 | 26.0 | 9750 | 1.0465 | 0.885 |
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+ | 0.0249 | 27.0 | 10125 | 1.0322 | 0.8833 |
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+ | 0.0001 | 28.0 | 10500 | 1.0170 | 0.8917 |
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+ | 0.0002 | 29.0 | 10875 | 1.0011 | 0.8933 |
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+ | 0.0298 | 30.0 | 11250 | 1.0259 | 0.89 |
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+ | 0.006 | 31.0 | 11625 | 1.0094 | 0.8867 |
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+ | 0.0305 | 32.0 | 12000 | 1.0462 | 0.885 |
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+ | 0.0001 | 33.0 | 12375 | 1.0305 | 0.8917 |
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+ | 0.0011 | 34.0 | 12750 | 1.0273 | 0.89 |
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+ | 0.0001 | 35.0 | 13125 | 1.0726 | 0.89 |
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+ | 0.0022 | 36.0 | 13500 | 1.0573 | 0.8917 |
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+ | 0.0086 | 37.0 | 13875 | 1.0713 | 0.8833 |
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+ | 0.0263 | 38.0 | 14250 | 1.0516 | 0.89 |
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+ | 0.0001 | 39.0 | 14625 | 1.0815 | 0.8867 |
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+ | 0.0002 | 40.0 | 15000 | 1.0626 | 0.8833 |
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+ | 0.0099 | 41.0 | 15375 | 1.0610 | 0.8883 |
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+ | 0.0004 | 42.0 | 15750 | 1.0801 | 0.885 |
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+ | 0.0001 | 43.0 | 16125 | 1.0685 | 0.8867 |
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+ | 0.007 | 44.0 | 16500 | 1.0616 | 0.8967 |
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+ | 0.0 | 45.0 | 16875 | 1.0741 | 0.8917 |
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+ | 0.0172 | 46.0 | 17250 | 1.0800 | 0.8883 |
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+ | 0.004 | 47.0 | 17625 | 1.0888 | 0.89 |
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+ | 0.0113 | 48.0 | 18000 | 1.0832 | 0.8867 |
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+ | 0.0002 | 49.0 | 18375 | 1.0808 | 0.8867 |
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+ | 0.0001 | 50.0 | 18750 | 1.0796 | 0.8883 |
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  ### Framework versions
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