I have the weights of a model which uses semantic segmentation in floor plan images to detect things like: walls, windows, type of rooms.
I have a new dataset of floor plan images and I want to train a model, using Mask-RCNN and the weights of the previous mentioned model.
Is that possible? Or do the annotations have to be equal (same number of instances)?
Thank you
Some details about the previous mentioned model:
semantic segmentation
classes for walls windows, types of rooms (kitchen,bathroom)
New dataset:
annotation for each instance
classes for walls, windows, doors, columns, types of rooms(kitchen, bedroom, bathroom, office, etc), office seat, sink, toilet, bike parking, etc.
Hi
I have the weights of a model which uses semantic segmentation in floor plan images to detect things like: walls, windows, type of rooms.
I have a new dataset of floor plan images and I want to train a model, using Mask-RCNN and the weights of the previous mentioned model.
Is that possible? Or do the annotations have to be equal (same number of instances)?
Thank you
Some details about the previous mentioned model: semantic segmentation classes for walls windows, types of rooms (kitchen,bathroom)
New dataset: annotation for each instance classes for walls, windows, doors, columns, types of rooms(kitchen, bedroom, bathroom, office, etc), office seat, sink, toilet, bike parking, etc.