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@@ -14,9 +14,10 @@ First filtering was applied to remove non-character focused images, then croppin
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  central character(s) of each image. Both steps were performed automatically by two different vision models trained on manually labelled subsets of the original dataset.
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  This technique provided satisfactory results, however there is much room for improvement.
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- ## Preprocessing Datasets
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- [db-sfw-512-character-filter-dataset](https://huggingface.co/datasets/hayden-donnelly/db-sfw-512-character-filter-dataset) was used to train the character filter model and
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- [db-sfw-512-crop-dataset](https://huggingface.co/datasets/hayden-donnelly/db-sfw-512-crop-dataset) was used to train the crop model.
 
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  ## Original Dataset Citation
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  central character(s) of each image. Both steps were performed automatically by two different vision models trained on manually labelled subsets of the original dataset.
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  This technique provided satisfactory results, however there is much room for improvement.
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+ ## Dataset Preprocessing
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+
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+ - [db-sfw-512-character-filter-dataset](https://huggingface.co/datasets/hayden-donnelly/db-sfw-512-character-filter-dataset) was used to train the character filter model.
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+ - [db-sfw-512-crop-dataset](https://huggingface.co/datasets/hayden-donnelly/db-sfw-512-crop-dataset) was used to train the crop model.
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  ## Original Dataset Citation
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