Multiple regression for bbox only in fastai v1

[ilovescience (Tanishq)]

I found this:

Regression to single number

Just pass label_cls=FloatList. The Points classes are for points, so two coordinates, and more specifically ImagePoints (when you need to apply data augmentation to the points the same way as the image). For all other regression problems, FloatList is the label class you need.

So try this:

data = (ImageItemList.from_csv(path=path, folder=‘train’, csv_name=‘tmp/bb.csv’)
.split_by_rand_pct()
.label_from_df(cols=[‘b1’,‘b2’,‘b3’,‘b4’],label_cls=FloatList)
.transform(get_transforms(), size=sz)
.databunch(bs=bs).normalize(imagenet_stats)
)

Only problem is that I don’t think it would do the proper transforms on the values, so you probably couldn’t do any rotation transforms.

The alternative would be to use PointsItemList but have the columns be the four points, rather than just two points and width and height. Maybe because of passing just two points and height and width, it is getting confused and is not recognizing it as Image Points?