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@@ -59,11 +59,7 @@ COCO 2017 unlabeled from 123'403 images -> 8'814 tiles.
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  COCO 2017 train from 118'287 images -> 8'442 tiles.
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  And in some cases this led to more images, because the original images were high resolution and therefore gave multiple 512x512 tiles per single image.
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- For example HQ50K -> 213'396 tiles.
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-
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- ## Conversion
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- If the images in the dataset were in the jpg format, they have been converted to png format using [Mogrify](https://imagemagick.org/script/mogrify.php).
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  ## BHI Filtering
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@@ -90,7 +86,7 @@ Nomos_Uni -> 2'466 Tiles
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  Nomosv2 -> 5'226 Tiles
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  inaturalist_2019 -> 131'943 Tiles
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- My main point here also would be that this dataset, even though still consisting of like 390k tiles, is already a strongly reduced version of these original datasets combined.
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  ## Files
 
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  COCO 2017 train from 118'287 images -> 8'442 tiles.
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  And in some cases this led to more images, because the original images were high resolution and therefore gave multiple 512x512 tiles per single image.
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+ For example HQ50K -> 213'396 tiles.
 
 
 
 
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  ## BHI Filtering
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  Nomosv2 -> 5'226 Tiles
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  inaturalist_2019 -> 131'943 Tiles
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+ My main point here also would be that this dataset, even though still consisting of around 300k tiles, is already a strongly reduced version of these original datasets combined.
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  ## Files