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rukundob451
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convnext-tiny-224-finetuned-papsmear

Image Classification
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convnext
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  1. README.md +19 -19
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@@ -23,7 +23,7 @@ model-index:
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  metrics:
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  - name: Accuracy
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  type: accuracy
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- value: 0.8529411764705882
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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
@@ -33,8 +33,8 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 0.4485
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- - Accuracy: 0.8529
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  ## Model description
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@@ -68,26 +68,26 @@ 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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- | 1.4789 | 0.9935 | 38 | 1.4474 | 0.3235 |
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- | 1.2481 | 1.9869 | 76 | 1.0898 | 0.6397 |
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- | 0.9694 | 2.9804 | 114 | 0.9261 | 0.6471 |
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- | 0.8414 | 4.0 | 153 | 0.8016 | 0.7059 |
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- | 0.7675 | 4.9935 | 191 | 0.7258 | 0.7426 |
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- | 0.7131 | 5.9869 | 229 | 0.7147 | 0.7353 |
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- | 0.5973 | 6.9804 | 267 | 0.7040 | 0.7353 |
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- | 0.5543 | 8.0 | 306 | 0.6064 | 0.7941 |
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- | 0.4903 | 8.9935 | 344 | 0.5418 | 0.8382 |
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- | 0.3942 | 9.9869 | 382 | 0.5043 | 0.8456 |
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- | 0.3721 | 10.9804 | 420 | 0.4922 | 0.8456 |
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- | 0.4036 | 12.0 | 459 | 0.4944 | 0.8309 |
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- | 0.3444 | 12.9935 | 497 | 0.4760 | 0.8529 |
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- | 0.3385 | 13.9869 | 535 | 0.4597 | 0.8603 |
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- | 0.3161 | 14.9020 | 570 | 0.4485 | 0.8529 |
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  ### Framework versions
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  - Transformers 4.44.2
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  - Pytorch 2.4.1+cu121
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- - Datasets 3.0.0
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  - Tokenizers 0.19.1
 
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  metrics:
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  - name: Accuracy
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  type: accuracy
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+ value: 0.8088235294117647
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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 [facebook/convnext-tiny-224](https://huggingface.co/facebook/convnext-tiny-224) on the imagefolder dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 0.4893
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+ - Accuracy: 0.8088
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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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+ | 1.706 | 0.9935 | 38 | 1.6028 | 0.2794 |
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+ | 1.3867 | 1.9869 | 76 | 1.2961 | 0.4853 |
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+ | 1.0784 | 2.9804 | 114 | 1.0588 | 0.5221 |
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+ | 0.9128 | 4.0 | 153 | 0.8886 | 0.6618 |
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+ | 0.7466 | 4.9935 | 191 | 0.8913 | 0.6029 |
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+ | 0.6886 | 5.9869 | 229 | 0.7380 | 0.7059 |
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+ | 0.6198 | 6.9804 | 267 | 0.7622 | 0.7132 |
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+ | 0.6001 | 8.0 | 306 | 0.7083 | 0.6838 |
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+ | 0.5542 | 8.9935 | 344 | 0.5909 | 0.7721 |
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+ | 0.5161 | 9.9869 | 382 | 0.5909 | 0.7574 |
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+ | 0.4631 | 10.9804 | 420 | 0.5677 | 0.7721 |
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+ | 0.4284 | 12.0 | 459 | 0.5229 | 0.7868 |
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+ | 0.4334 | 12.9935 | 497 | 0.5160 | 0.8015 |
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+ | 0.4386 | 13.9869 | 535 | 0.4788 | 0.8015 |
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+ | 0.3728 | 14.9020 | 570 | 0.4893 | 0.8088 |
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  ### Framework versions
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  - Transformers 4.44.2
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  - Pytorch 2.4.1+cu121
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+ - Datasets 3.0.1
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  - Tokenizers 0.19.1