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CygnusX1 t1_j2jxcl2 wrote

What are techniques or best practices for detecting/segmenting large objects in high resolution images? Some problems I run into are training with large image chip sizes (e.g. 1024x1024) to make sure the entire object can fit on a chip, culminating in GPU memory management pains. I've been using MaskRCNN.

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debrises t1_j3irf9s wrote

>What are techniques or best practices for detecting/segmenting large objects in high resolution images? Some problems I run into are training with large image chip sizes

The first thing that came to my mind was gradient accumulation if you have limited GPU memory. Fitting an image of that size on a single GPU could result in a very small batch, which is not so good for training speed and stability.

PyTorch lightning offers such a feature if you're using PyTorch.

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MegavirusOfDoom t1_j4cro9x wrote

Check how the magic wand works on github and open source code for Gimp. There are probably a lot of specific terminologies for these selection algorythms, and when you have found descriptions of pros working on the field you will have access to a lot of their research.

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