Before someone sends me to the model training repo, please let me just explain.

I want to fine tune the existing model, say PubLayNet/faster_rcnn_R_50_FPN_3x model for my own task BUT for a Single Class, ex: text Detection only where the mapping is as {0: "Text", 1: "Title", 2: "List", 3:"Table", 4:"Figure"}

Or maybe on HJDataset where classes are {1:"Page Frame", 2:"Row", 3:"Title Region", 4:"Text Region", 5:"Title", 6:"Subtitle", 7:"Other"}.

I found this Kaggle Notebook on fine tuning with Detectron2 for fine tuning but the problem is what I have described earlier that I just want to train on 1 class.

What would be the changes that I'll have to do? How would the things_classes look like?

thing_classes= ['text'] # cfg.MODEL.ROI_HEADS.NUM_CLASSES = 1
thing_classes= ["None",'text'] # cfg.MODEL.ROI_HEADS.NUM_CLASSES = 2
thing_classes= ['text', 'None', 'None', 'None', 'None'] # cfg.MODEL.ROI_HEADS.NUM_CLASSES = 5
thing_classes= ['None', 'text', 'None', 'None', 'None', 'None'] # cfg.MODEL.ROI_HEADS.NUM_CLASSES = 6
thing_classes= ['text', 'None', 'None', 'None', 'None', 'None'] # cfg.MODEL.ROI_HEADS.NUM_CLASSES = 6

2. Would it be any different if I use Layout Parser Model Config for faster_rcnn_R_50_FPN_3x instead of the default one from Detectron2/configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml

Thanks in advance.