I'm trying to build a floor type image classification model.There's an open dataset called OpenSurfaces containing images segmented by the material type of every item appearing on a room.
Something like this:
I thought that using this dataset to train a floor detection model would be a good thing, so I wrote a script to extract the materials I'd like to detect (wood, tile, carpet, marble, stone, ...). These are some examples of the images I've got as a result of the script:
Then I trained a CNN but I've only got something like 70% of accuracy and I don't really now if I'm going along the right path. Is it better to train a model with the pictures I extracted or it'd be better to train it with the full room image, not the segmented part?
I'm quite lost, so any guidance will be greatly appreciated.