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  1. README.md +21 -21
  2. model.safetensors +1 -1
  3. runs/Nov24_23-46-34_b02d3efcfbf8/events.out.tfevents.1700870000.b02d3efcfbf8.865.0 +2 -2
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: 0.8398058252427184
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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 [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: 0.5298
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- - Accuracy: 0.8398
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  ## Model description
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@@ -67,26 +67,26 @@ 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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- | 1.6631 | 0.99 | 106 | 1.4344 | 0.5494 |
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- | 1.069 | 2.0 | 213 | 0.8917 | 0.7282 |
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- | 0.801 | 3.0 | 320 | 0.7612 | 0.7533 |
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- | 0.6075 | 4.0 | 427 | 0.6522 | 0.7921 |
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- | 0.5046 | 4.99 | 533 | 0.6085 | 0.8005 |
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- | 0.4018 | 6.0 | 640 | 0.6132 | 0.8023 |
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- | 0.3641 | 7.0 | 747 | 0.5510 | 0.8292 |
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- | 0.4003 | 8.0 | 854 | 0.5304 | 0.8248 |
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- | 0.3142 | 8.99 | 960 | 0.5271 | 0.8350 |
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- | 0.2266 | 10.0 | 1067 | 0.5450 | 0.8363 |
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- | 0.1572 | 11.0 | 1174 | 0.5317 | 0.8323 |
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- | 0.1891 | 12.0 | 1281 | 0.5269 | 0.8363 |
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- | 0.1551 | 12.99 | 1387 | 0.5340 | 0.8376 |
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- | 0.1548 | 14.0 | 1494 | 0.5313 | 0.8367 |
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- | 0.1781 | 14.89 | 1590 | 0.5298 | 0.8398 |
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  ### Framework versions
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- - Transformers 4.35.1
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  - Pytorch 2.1.0+cu118
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- - Datasets 2.14.7
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- - Tokenizers 0.14.1
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8495867768595041
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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: 0.4812
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+ - Accuracy: 0.8496
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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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+ | 1.4669 | 1.0 | 88 | 1.3067 | 0.5612 |
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+ | 0.8898 | 1.99 | 176 | 0.8380 | 0.7140 |
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+ | 0.7243 | 2.99 | 264 | 0.6559 | 0.7694 |
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+ | 0.5158 | 4.0 | 353 | 0.5982 | 0.7950 |
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+ | 0.4605 | 5.0 | 441 | 0.5856 | 0.8083 |
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+ | 0.332 | 5.99 | 529 | 0.5138 | 0.8355 |
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+ | 0.3375 | 6.99 | 617 | 0.5095 | 0.8264 |
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+ | 0.2188 | 8.0 | 706 | 0.5089 | 0.8322 |
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+ | 0.2112 | 9.0 | 794 | 0.5126 | 0.8380 |
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+ | 0.1895 | 9.99 | 882 | 0.5057 | 0.8364 |
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+ | 0.1593 | 10.99 | 970 | 0.4852 | 0.8529 |
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+ | 0.1463 | 12.0 | 1059 | 0.4934 | 0.8430 |
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+ | 0.1565 | 13.0 | 1147 | 0.4794 | 0.8496 |
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+ | 0.1236 | 13.99 | 1235 | 0.4863 | 0.8463 |
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+ | 0.1407 | 14.96 | 1320 | 0.4812 | 0.8496 |
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  ### Framework versions
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+ - Transformers 4.35.2
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  - Pytorch 2.1.0+cu118
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+ - Datasets 2.15.0
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+ - Tokenizers 0.15.0
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