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vit-base-patch16-224-Trial008-YEL_STEM

Image Classification
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PyTorch
vit
Generated from Trainer
Eval Results
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@@ -21,7 +21,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 1.0
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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
@@ -31,8 +31,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-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: 0.1937
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- - Accuracy: 1.0
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  ## Model description
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@@ -66,56 +66,40 @@ 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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- | 0.7185 | 1.0 | 1 | 0.7706 | 0.5926 |
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- | 0.7119 | 2.0 | 2 | 0.6863 | 0.5556 |
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- | 0.6949 | 3.0 | 3 | 0.6335 | 0.6296 |
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- | 0.6605 | 4.0 | 4 | 0.5917 | 0.7037 |
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- | 0.6505 | 5.0 | 5 | 0.5240 | 0.7407 |
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- | 0.59 | 6.0 | 6 | 0.5366 | 0.7037 |
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- | 0.6172 | 7.0 | 7 | 0.4609 | 0.7407 |
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- | 0.5515 | 8.0 | 8 | 0.3625 | 0.8889 |
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- | 0.5121 | 9.0 | 9 | 0.3239 | 0.8889 |
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- | 0.5379 | 10.0 | 10 | 0.3688 | 0.8519 |
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- | 0.4648 | 11.0 | 11 | 0.3545 | 0.8148 |
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- | 0.4653 | 12.0 | 12 | 0.2484 | 0.9259 |
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- | 0.4433 | 13.0 | 13 | 0.1937 | 1.0 |
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- | 0.4537 | 14.0 | 14 | 0.1894 | 0.9630 |
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- | 0.427 | 15.0 | 15 | 0.2249 | 0.8519 |
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- | 0.4154 | 16.0 | 16 | 0.1589 | 0.9630 |
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- | 0.3895 | 17.0 | 17 | 0.1041 | 1.0 |
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- | 0.3994 | 18.0 | 18 | 0.0916 | 1.0 |
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- | 0.3692 | 19.0 | 19 | 0.0914 | 1.0 |
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- | 0.3647 | 20.0 | 20 | 0.1464 | 0.8889 |
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- | 0.3789 | 21.0 | 21 | 0.1525 | 0.9259 |
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- | 0.3889 | 22.0 | 22 | 0.0997 | 1.0 |
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- | 0.3312 | 23.0 | 23 | 0.0698 | 1.0 |
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- | 0.3653 | 24.0 | 24 | 0.0650 | 1.0 |
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- | 0.3499 | 25.0 | 25 | 0.0626 | 1.0 |
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- | 0.3602 | 26.0 | 26 | 0.0732 | 1.0 |
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- | 0.3209 | 27.0 | 27 | 0.0622 | 1.0 |
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- | 0.3 | 28.0 | 28 | 0.0544 | 1.0 |
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- | 0.2738 | 29.0 | 29 | 0.0448 | 1.0 |
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- | 0.3283 | 30.0 | 30 | 0.0430 | 1.0 |
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- | 0.3162 | 31.0 | 31 | 0.0402 | 1.0 |
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- | 0.3411 | 32.0 | 32 | 0.0394 | 1.0 |
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- | 0.3195 | 33.0 | 33 | 0.0381 | 1.0 |
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- | 0.3111 | 34.0 | 34 | 0.0350 | 1.0 |
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- | 0.2816 | 35.0 | 35 | 0.0350 | 1.0 |
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- | 0.2602 | 36.0 | 36 | 0.0358 | 1.0 |
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- | 0.3128 | 37.0 | 37 | 0.0388 | 1.0 |
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- | 0.326 | 38.0 | 38 | 0.0498 | 1.0 |
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- | 0.3228 | 39.0 | 39 | 0.0702 | 1.0 |
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- | 0.3073 | 40.0 | 40 | 0.0782 | 0.9630 |
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- | 0.3266 | 41.0 | 41 | 0.0721 | 0.9630 |
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- | 0.3546 | 42.0 | 42 | 0.0579 | 1.0 |
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- | 0.2832 | 43.0 | 43 | 0.0487 | 1.0 |
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- | 0.2872 | 44.0 | 44 | 0.0428 | 1.0 |
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- | 0.2699 | 45.0 | 45 | 0.0395 | 1.0 |
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- | 0.3002 | 46.0 | 46 | 0.0391 | 1.0 |
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- | 0.327 | 47.0 | 47 | 0.0390 | 1.0 |
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- | 0.2746 | 48.0 | 48 | 0.0387 | 1.0 |
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- | 0.2781 | 49.0 | 49 | 0.0386 | 1.0 |
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- | 0.2925 | 50.0 | 50 | 0.0386 | 1.0 |
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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: 0.9411764705882353
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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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  This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-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: 0.0958
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+ - Accuracy: 0.9412
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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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+ | 0.544 | 1.0 | 1 | 0.8179 | 0.4118 |
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+ | 0.3416 | 2.0 | 3 | 0.7448 | 0.5294 |
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+ | 0.1412 | 3.0 | 5 | 0.7606 | 0.5294 |
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+ | 0.4868 | 4.0 | 6 | 0.5647 | 0.6471 |
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+ | 0.3852 | 5.0 | 7 | 0.4646 | 0.8235 |
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+ | 0.284 | 6.0 | 9 | 0.4300 | 0.8235 |
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+ | 0.1075 | 7.0 | 11 | 0.4628 | 0.8235 |
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+ | 0.3243 | 8.0 | 12 | 0.4687 | 0.7647 |
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+ | 0.3317 | 9.0 | 13 | 0.4089 | 0.8235 |
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+ | 0.146 | 10.0 | 15 | 0.3330 | 0.8824 |
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+ | 0.0762 | 11.0 | 17 | 0.2941 | 0.8824 |
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+ | 0.2351 | 12.0 | 18 | 0.3217 | 0.8824 |
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+ | 0.2458 | 13.0 | 19 | 0.3705 | 0.8824 |
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+ | 0.1431 | 14.0 | 21 | 0.3138 | 0.8824 |
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+ | 0.0883 | 15.0 | 23 | 0.1510 | 0.9412 |
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+ | 0.1601 | 16.0 | 24 | 0.1373 | 0.9412 |
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+ | 0.2212 | 17.0 | 25 | 0.1175 | 0.9412 |
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+ | 0.1311 | 18.0 | 27 | 0.1130 | 0.9412 |
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+ | 0.0801 | 19.0 | 29 | 0.1506 | 0.9412 |
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+ | 0.1857 | 20.0 | 30 | 0.1272 | 0.9412 |
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+ | 0.241 | 21.0 | 31 | 0.0974 | 0.9412 |
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+ | 0.1098 | 22.0 | 33 | 0.0593 | 1.0 |
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+ | 0.0464 | 23.0 | 35 | 0.0574 | 1.0 |
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+ | 0.1757 | 24.0 | 36 | 0.0554 | 1.0 |
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+ | 0.1992 | 25.0 | 37 | 0.0605 | 1.0 |
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+ | 0.1167 | 26.0 | 39 | 0.0818 | 0.9412 |
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+ | 0.0703 | 27.0 | 41 | 0.1177 | 0.9412 |
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+ | 0.1382 | 28.0 | 42 | 0.1281 | 0.9412 |
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+ | 0.1563 | 29.0 | 43 | 0.1357 | 0.9412 |
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+ | 0.1113 | 30.0 | 45 | 0.1417 | 0.8824 |
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+ | 0.0639 | 31.0 | 47 | 0.1250 | 0.9412 |
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+ | 0.1564 | 32.0 | 48 | 0.1107 | 0.9412 |
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+ | 0.1877 | 33.0 | 49 | 0.1002 | 0.9412 |
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+ | 0.06 | 33.33 | 50 | 0.0958 | 0.9412 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Framework versions