I asked a similar question before but didn’t get any responses – I’m not sure if that’s because my question was too vague, was posted at the wrong time, or wasn’t answerable.
So I’m sort of re-wording it and posting it again in hope of pointers.
Here’s my machine learning problem:
I have images of petri dishes containing creatures (sea lice). I’ve segmented all of the lice in each image (using labelbox).
Each louse has the label of ‘louse’, but also the further label / sub-classes of:
- “Male” or “Female”
- “Preadult” or “Adult”
I want to figure out how to achieve the following things using fastai:
- Count how many lice are in each image
- Learn their correct ‘sub classes’ (eg, "This louse is ‘Female’ and ‘Adult’ ")
- Calculate the mean grey value of each louse (convert to 8bit greyscale – to measure ‘how light/dark’ the louse is)
- Try to calculate the louse size/area
Does anybody have any pointers for how to start on this (aside from preparing the data which I’m already doing) ?
I’ve seen that there are some non-fastai libraries that might be able to handle this, but my fastai/Python skills aren’t advanced enough to integrate these libraries with fastai.
Any pointers would be really useful! Thanks in advance!