Multi-label instance segmentation

Hi all,
I’m an undergrad working on my senior project which is building a vision-based strawberry inspection system. I used detectron2 to train a model to detect instances of strawberries of various classes in images, prior to that I also trained a model to just identify strawberries in images(which I used to do model-assisted labeling to build the dataset i’m using). But I was wondering if there was a relatively easy way to modify the Mask R-CNN architecture to attach multiple class labels to an given object.
This would be useful for this use-case because I want to identify all the instances of strawberries in the image, but I also want to tag each berry with quality criteria i.e. (green, underripe, overripe, damaged, moldy,… ) there are a lot of berries that have overlapping quality criteria and having the model infer the dominant class seems like a much more noisy problem to solve. Any help would be greatly appreciated!


Hi Avi, cool project! The easiest approach might be to train a separate image classification model that can classify strawberries into the categories you’re interested in, and in the inference stage just run this second model on your detectron2 output. Is there a particular reason you’re looking to combine both in the same model instead of taking this easier road?