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Update README.md

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@@ -11,10 +11,12 @@ The Danbooru dataset is a wealth of (mostly) free anime style images,
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  The problem being that the images are indiscriminately included.
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  Some pics are explicitly copyrighted and shouldnt be in it.
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  Some have watermarks.
 
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  Some just frankly arent good.
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  But the rest... are *really good*
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  So, this is a public effort to pull out just the clean, AI-training-usable images.
 
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  We are working from
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  https://huggingface.co/datasets/animelover/danbooru2022/commits/main/data
@@ -26,7 +28,7 @@ So now we copy a block of them, and delete the "bad" images. One zip file at a t
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  There are literally hundreds of blocks of data, each block having perhaps 4000-5000 images.
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  So this is an organized, crowd-sourced volunteer effort.
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- # Usage tips:
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  While he images should be "usable" for AI training, and they are all individually tagged with .txt files,
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  if you just dumped them all into a trainer, the results would probably not be coherent.
@@ -36,4 +38,4 @@ subject matter, or style, and pull those specific ones out for training.
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  You may also want to add in your own custom tags, on top of the ones that are already there.
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  # Currently spoken-for segments:
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- * 0000-0010: ppbrown (data-0001 complete)
 
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  The problem being that the images are indiscriminately included.
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  Some pics are explicitly copyrighted and shouldnt be in it.
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  Some have watermarks.
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+ Some have legally questionable subject matter.
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  Some just frankly arent good.
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  But the rest... are *really good*
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  So, this is a public effort to pull out just the clean, AI-training-usable images.
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+ Note that we do not remove a signature or watermark from an image. We just exclude the image entirely from the set.
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  We are working from
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  https://huggingface.co/datasets/animelover/danbooru2022/commits/main/data
 
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  There are literally hundreds of blocks of data, each block having perhaps 4000-5000 images.
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  So this is an organized, crowd-sourced volunteer effort.
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+ # Training tips:
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  While he images should be "usable" for AI training, and they are all individually tagged with .txt files,
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  if you just dumped them all into a trainer, the results would probably not be coherent.
 
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  You may also want to add in your own custom tags, on top of the ones that are already there.
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  # Currently spoken-for segments:
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+ * 0000-0010: ppbrown (data-0001 - data-0002 complete)