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hkivancoral
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smids_3x_beit_base_adamax_00001_fold5

Image Classification
Transformers
PyTorch
beit
Generated from Trainer
Eval Results
Inference Endpoints
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End of training

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  1. README.md +51 -51
  2. pytorch_model.bin +1 -1
README.md CHANGED
@@ -32,7 +32,7 @@ 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: 0.7471
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  - Accuracy: 0.9083
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  ## Model description
@@ -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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- | 0.342 | 1.0 | 225 | 0.2883 | 0.8833 |
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- | 0.2244 | 2.0 | 450 | 0.2463 | 0.9083 |
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- | 0.1906 | 3.0 | 675 | 0.2773 | 0.905 |
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- | 0.095 | 4.0 | 900 | 0.2495 | 0.9033 |
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- | 0.0664 | 5.0 | 1125 | 0.2917 | 0.9033 |
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- | 0.0453 | 6.0 | 1350 | 0.2947 | 0.9067 |
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- | 0.0969 | 7.0 | 1575 | 0.3330 | 0.9117 |
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- | 0.0514 | 8.0 | 1800 | 0.3802 | 0.9117 |
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- | 0.1005 | 9.0 | 2025 | 0.4648 | 0.9083 |
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- | 0.0165 | 10.0 | 2250 | 0.4638 | 0.895 |
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- | 0.0269 | 11.0 | 2475 | 0.5851 | 0.8883 |
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- | 0.0652 | 12.0 | 2700 | 0.5785 | 0.8917 |
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- | 0.081 | 13.0 | 2925 | 0.5841 | 0.8983 |
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- | 0.0377 | 14.0 | 3150 | 0.5798 | 0.905 |
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- | 0.03 | 15.0 | 3375 | 0.6329 | 0.89 |
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- | 0.0242 | 16.0 | 3600 | 0.5810 | 0.895 |
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- | 0.0266 | 17.0 | 3825 | 0.5680 | 0.9167 |
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- | 0.0329 | 18.0 | 4050 | 0.5701 | 0.9183 |
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- | 0.0582 | 19.0 | 4275 | 0.6235 | 0.9083 |
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- | 0.0028 | 20.0 | 4500 | 0.6525 | 0.905 |
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- | 0.0003 | 21.0 | 4725 | 0.6853 | 0.9067 |
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- | 0.022 | 22.0 | 4950 | 0.6544 | 0.905 |
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- | 0.0045 | 23.0 | 5175 | 0.6759 | 0.9033 |
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- | 0.0351 | 24.0 | 5400 | 0.6446 | 0.9117 |
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- | 0.0333 | 25.0 | 5625 | 0.6670 | 0.9083 |
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- | 0.0143 | 26.0 | 5850 | 0.6937 | 0.91 |
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- | 0.008 | 27.0 | 6075 | 0.6634 | 0.9083 |
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- | 0.0281 | 28.0 | 6300 | 0.6712 | 0.9133 |
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- | 0.0182 | 29.0 | 6525 | 0.6482 | 0.9083 |
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- | 0.0111 | 30.0 | 6750 | 0.7204 | 0.905 |
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- | 0.0002 | 31.0 | 6975 | 0.7191 | 0.9067 |
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- | 0.0002 | 32.0 | 7200 | 0.7356 | 0.9 |
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- | 0.0144 | 33.0 | 7425 | 0.6757 | 0.905 |
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- | 0.0023 | 34.0 | 7650 | 0.6796 | 0.9067 |
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- | 0.0353 | 35.0 | 7875 | 0.7115 | 0.905 |
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- | 0.0002 | 36.0 | 8100 | 0.6973 | 0.9117 |
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- | 0.0038 | 37.0 | 8325 | 0.7036 | 0.9067 |
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- | 0.0007 | 38.0 | 8550 | 0.7201 | 0.91 |
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- | 0.0032 | 39.0 | 8775 | 0.7280 | 0.91 |
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- | 0.0003 | 40.0 | 9000 | 0.7519 | 0.9067 |
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- | 0.0013 | 41.0 | 9225 | 0.7411 | 0.8983 |
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- | 0.0003 | 42.0 | 9450 | 0.7547 | 0.91 |
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- | 0.0145 | 43.0 | 9675 | 0.7708 | 0.905 |
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- | 0.0125 | 44.0 | 9900 | 0.7613 | 0.905 |
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- | 0.001 | 45.0 | 10125 | 0.7388 | 0.91 |
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- | 0.0492 | 46.0 | 10350 | 0.7435 | 0.9033 |
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- | 0.0003 | 47.0 | 10575 | 0.7445 | 0.9083 |
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- | 0.002 | 48.0 | 10800 | 0.7444 | 0.9083 |
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- | 0.0063 | 49.0 | 11025 | 0.7475 | 0.91 |
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- | 0.0048 | 50.0 | 11250 | 0.7471 | 0.9083 |
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  ### Framework versions
 
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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: 0.7465
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  - Accuracy: 0.9083
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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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+ | 0.2292 | 1.0 | 375 | 0.3140 | 0.8683 |
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+ | 0.2093 | 2.0 | 750 | 0.2336 | 0.9067 |
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+ | 0.1226 | 3.0 | 1125 | 0.2477 | 0.9117 |
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+ | 0.0876 | 4.0 | 1500 | 0.2419 | 0.92 |
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+ | 0.0679 | 5.0 | 1875 | 0.3315 | 0.8983 |
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+ | 0.0323 | 6.0 | 2250 | 0.3578 | 0.9033 |
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+ | 0.0877 | 7.0 | 2625 | 0.4065 | 0.91 |
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+ | 0.0394 | 8.0 | 3000 | 0.4499 | 0.91 |
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+ | 0.0357 | 9.0 | 3375 | 0.4692 | 0.9167 |
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+ | 0.0126 | 10.0 | 3750 | 0.5130 | 0.9083 |
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+ | 0.0533 | 11.0 | 4125 | 0.4610 | 0.915 |
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+ | 0.018 | 12.0 | 4500 | 0.5545 | 0.9167 |
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+ | 0.0014 | 13.0 | 4875 | 0.6258 | 0.905 |
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+ | 0.0294 | 14.0 | 5250 | 0.5991 | 0.9133 |
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+ | 0.0147 | 15.0 | 5625 | 0.5948 | 0.91 |
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+ | 0.0096 | 16.0 | 6000 | 0.6032 | 0.905 |
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+ | 0.03 | 17.0 | 6375 | 0.6625 | 0.9017 |
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+ | 0.0009 | 18.0 | 6750 | 0.6142 | 0.9067 |
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+ | 0.0073 | 19.0 | 7125 | 0.7447 | 0.8917 |
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+ | 0.0002 | 20.0 | 7500 | 0.6954 | 0.9067 |
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+ | 0.0056 | 21.0 | 7875 | 0.7051 | 0.9133 |
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+ | 0.0361 | 22.0 | 8250 | 0.6457 | 0.9067 |
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+ | 0.0618 | 23.0 | 8625 | 0.6671 | 0.905 |
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+ | 0.0003 | 24.0 | 9000 | 0.7426 | 0.905 |
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+ | 0.0152 | 25.0 | 9375 | 0.6683 | 0.905 |
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+ | 0.0005 | 26.0 | 9750 | 0.7087 | 0.9067 |
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+ | 0.0002 | 27.0 | 10125 | 0.7480 | 0.905 |
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+ | 0.004 | 28.0 | 10500 | 0.7511 | 0.905 |
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+ | 0.0094 | 29.0 | 10875 | 0.7080 | 0.9017 |
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+ | 0.0056 | 30.0 | 11250 | 0.7631 | 0.905 |
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+ | 0.0441 | 31.0 | 11625 | 0.7580 | 0.9083 |
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+ | 0.0024 | 32.0 | 12000 | 0.7682 | 0.9083 |
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+ | 0.0002 | 33.0 | 12375 | 0.7496 | 0.9133 |
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+ | 0.0005 | 34.0 | 12750 | 0.7614 | 0.9067 |
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+ | 0.0057 | 35.0 | 13125 | 0.7635 | 0.9083 |
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+ | 0.0004 | 36.0 | 13500 | 0.7425 | 0.9117 |
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+ | 0.0153 | 37.0 | 13875 | 0.7300 | 0.91 |
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+ | 0.0003 | 38.0 | 14250 | 0.7331 | 0.9083 |
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+ | 0.0149 | 39.0 | 14625 | 0.7175 | 0.905 |
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+ | 0.0093 | 40.0 | 15000 | 0.7444 | 0.9067 |
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+ | 0.0001 | 41.0 | 15375 | 0.7317 | 0.9117 |
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+ | 0.0 | 42.0 | 15750 | 0.7474 | 0.9033 |
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+ | 0.0002 | 43.0 | 16125 | 0.7578 | 0.905 |
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+ | 0.0004 | 44.0 | 16500 | 0.7636 | 0.905 |
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+ | 0.0 | 45.0 | 16875 | 0.7676 | 0.91 |
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+ | 0.0005 | 46.0 | 17250 | 0.7589 | 0.91 |
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+ | 0.0158 | 47.0 | 17625 | 0.7484 | 0.9083 |
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+ | 0.0001 | 48.0 | 18000 | 0.7568 | 0.91 |
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+ | 0.0001 | 49.0 | 18375 | 0.7501 | 0.9083 |
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+ | 0.0109 | 50.0 | 18750 | 0.7465 | 0.9083 |
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  ### Framework versions
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