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KietZer0
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ViT_Flower102_4

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vit
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  1. README.md +27 -27
  2. model.safetensors +1 -1
  3. runs/Apr04_04-38-31_17332283cf54/events.out.tfevents.1712207788.17332283cf54.1249.1 +2 -2
README.md CHANGED
@@ -20,11 +20,11 @@ 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-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.0651
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- - Accuracy: 0.9873
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- - Precision: 0.9873
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- - Recall: 0.9873
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- - F1: 0.9873
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  ## Model description
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@@ -55,28 +55,28 @@ The following hyperparameters were used during training:
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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- | 2.5217 | 0.22 | 100 | 2.8392 | 0.8363 | 0.8363 | 0.8363 | 0.8363 |
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- | 1.2157 | 0.45 | 200 | 1.5467 | 0.9304 | 0.9304 | 0.9304 | 0.9304 |
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- | 0.5702 | 0.67 | 300 | 0.7642 | 0.9598 | 0.9598 | 0.9598 | 0.9598 |
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- | 0.367 | 0.89 | 400 | 0.4966 | 0.9637 | 0.9637 | 0.9637 | 0.9637 |
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- | 0.1299 | 1.11 | 500 | 0.2458 | 0.9784 | 0.9784 | 0.9784 | 0.9784 |
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- | 0.1142 | 1.34 | 600 | 0.1678 | 0.9833 | 0.9833 | 0.9833 | 0.9833 |
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- | 0.043 | 1.56 | 700 | 0.1746 | 0.9706 | 0.9706 | 0.9706 | 0.9706 |
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- | 0.0683 | 1.78 | 800 | 0.1554 | 0.9745 | 0.9745 | 0.9745 | 0.9745 |
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- | 0.0364 | 2.0 | 900 | 0.1132 | 0.9843 | 0.9843 | 0.9843 | 0.9843 |
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- | 0.019 | 2.23 | 1000 | 0.0939 | 0.9843 | 0.9843 | 0.9843 | 0.9843 |
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- | 0.0535 | 2.45 | 1100 | 0.1033 | 0.9833 | 0.9833 | 0.9833 | 0.9833 |
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- | 0.0164 | 2.67 | 1200 | 0.0698 | 0.9902 | 0.9902 | 0.9902 | 0.9902 |
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- | 0.1128 | 2.9 | 1300 | 0.0810 | 0.9853 | 0.9853 | 0.9853 | 0.9853 |
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- | 0.0127 | 3.12 | 1400 | 0.0725 | 0.9873 | 0.9873 | 0.9873 | 0.9873 |
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- | 0.0112 | 3.34 | 1500 | 0.0702 | 0.9902 | 0.9902 | 0.9902 | 0.9902 |
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- | 0.0383 | 3.56 | 1600 | 0.0860 | 0.9843 | 0.9843 | 0.9843 | 0.9843 |
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- | 0.0091 | 3.79 | 1700 | 0.0750 | 0.9843 | 0.9843 | 0.9843 | 0.9843 |
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- | 0.0079 | 4.01 | 1800 | 0.0674 | 0.9882 | 0.9882 | 0.9882 | 0.9882 |
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- | 0.0083 | 4.23 | 1900 | 0.0659 | 0.9873 | 0.9873 | 0.9873 | 0.9873 |
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- | 0.0078 | 4.45 | 2000 | 0.0652 | 0.9882 | 0.9882 | 0.9882 | 0.9882 |
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- | 0.0073 | 4.68 | 2100 | 0.0652 | 0.9873 | 0.9873 | 0.9873 | 0.9873 |
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- | 0.0076 | 4.9 | 2200 | 0.0651 | 0.9873 | 0.9873 | 0.9873 | 0.9873 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.1042
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+ - Accuracy: 0.9814
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+ - Precision: 0.9814
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+ - Recall: 0.9814
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+ - F1: 0.9814
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  ## Model description
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  | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
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  |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
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+ | 0.006 | 0.22 | 100 | 0.0735 | 0.9863 | 0.9863 | 0.9863 | 0.9863 |
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+ | 0.0044 | 0.45 | 200 | 0.0720 | 0.9882 | 0.9882 | 0.9882 | 0.9882 |
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+ | 0.3589 | 0.67 | 300 | 0.5454 | 0.8902 | 0.8902 | 0.8902 | 0.8902 |
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+ | 0.401 | 0.89 | 400 | 0.6406 | 0.8676 | 0.8676 | 0.8676 | 0.8676 |
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+ | 0.1851 | 1.11 | 500 | 0.4838 | 0.8912 | 0.8912 | 0.8912 | 0.8912 |
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+ | 0.1116 | 1.34 | 600 | 0.3375 | 0.9245 | 0.9245 | 0.9245 | 0.9245 |
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+ | 0.2359 | 1.56 | 700 | 0.4032 | 0.9059 | 0.9059 | 0.9059 | 0.9059 |
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+ | 0.062 | 1.78 | 800 | 0.2356 | 0.9549 | 0.9549 | 0.9549 | 0.9549 |
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+ | 0.0221 | 2.0 | 900 | 0.2307 | 0.9559 | 0.9559 | 0.9559 | 0.9559 |
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+ | 0.0052 | 2.23 | 1000 | 0.1620 | 0.9676 | 0.9676 | 0.9676 | 0.9676 |
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+ | 0.0277 | 2.45 | 1100 | 0.1881 | 0.9676 | 0.9676 | 0.9676 | 0.9676 |
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+ | 0.0025 | 2.67 | 1200 | 0.1483 | 0.9735 | 0.9735 | 0.9735 | 0.9735 |
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+ | 0.0078 | 2.9 | 1300 | 0.1199 | 0.9794 | 0.9794 | 0.9794 | 0.9794 |
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+ | 0.002 | 3.12 | 1400 | 0.1343 | 0.9755 | 0.9755 | 0.9755 | 0.9755 |
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+ | 0.0035 | 3.34 | 1500 | 0.1247 | 0.9775 | 0.9775 | 0.9775 | 0.9775 |
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+ | 0.0245 | 3.56 | 1600 | 0.1116 | 0.9775 | 0.9775 | 0.9775 | 0.9775 |
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+ | 0.0015 | 3.79 | 1700 | 0.1099 | 0.9775 | 0.9775 | 0.9775 | 0.9775 |
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+ | 0.0013 | 4.01 | 1800 | 0.1089 | 0.9804 | 0.9804 | 0.9804 | 0.9804 |
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+ | 0.0014 | 4.23 | 1900 | 0.1081 | 0.9804 | 0.9804 | 0.9804 | 0.9804 |
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+ | 0.0013 | 4.45 | 2000 | 0.1076 | 0.9804 | 0.9804 | 0.9804 | 0.9804 |
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+ | 0.0012 | 4.68 | 2100 | 0.1075 | 0.9804 | 0.9804 | 0.9804 | 0.9804 |
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+ | 0.0013 | 4.9 | 2200 | 0.1042 | 0.9814 | 0.9814 | 0.9814 | 0.9814 |
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  ### Framework versions
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