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select_training_images.py gives 42201 non-preferred images, which is inconsistent with 21108 mentioned in paper. Is there any other parameter that can be used to exactly reproduce the count of preferred and non-preferred images?
select_training_images.py gives 42201 non-preferred images, which is inconsistent with 21108 mentioned in paper. Is there any other parameter that can be used to exactly reproduce the count of preferred and non-preferred images?