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Currently, I'm trying to write a costum dataloader in order to train on the HDF5 format of stacked numpy images to save preprocessing time. The problem is I don't find any understandable solution how to adapt a new dataloader instead of the 'register_coco_instances()' Function.
I already saw the documentary about the dataset_mapper. Isn't the Mapper a step further after the dataloader, because the mapper applies the changes to the dataset_dict and I only want to replace the register_coco_instances function to just use already loaded numpy images.
This discussion was converted from issue #2999 on May 04, 2021 10:15.
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Currently, I'm trying to write a costum dataloader in order to train on the HDF5 format of stacked numpy images to save preprocessing time. The problem is I don't find any understandable solution how to adapt a new dataloader instead of the 'register_coco_instances()' Function.
I already saw the documentary about the dataset_mapper. Isn't the Mapper a step further after the dataloader, because the mapper applies the changes to the dataset_dict and I only want to replace the register_coco_instances function to just use already loaded numpy images.
I hope someone can help me.
Thank you in advance!
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