Hi Michael!



 Michael-J98:

there are no objects in some of the training images, can this will work?


Yes, but how best to do this will depend on the overall structure of
your task.
First, to me, “object detection” means finding one or more objects in
an image (and reporting their locations, e.g., their bounding boxes).
This is often done by sliding a series “detection” windows across the
image and then running the sub-image in such a window through an
object classifier.  I won’t say more about such full-scale object detectors
(used in this sense) other than to note that they are built out of smaller
pieces.
If you have a three-class classification problem – e.g., this image
contains either a “person” or a “dog” or a "“shrubbery” – then, in
order to be able to accept “background-only” images, you would
add a fourth class, “background,” and train your classifier in the
usual way with all four classes.
If you have multi-label, three-class classification problem – e.g., this
image does or does not contain a “person” and also does or does
not contain a “dog” and also does or does not contain a “shrubbery”
(that is, your image can be “true” for any number of your three classes,
including for none of them or all of them) – then you don’t have to do
anything additional to account for training images containing no
objects (other than to make sure that they are labelled correctly).
Best.
K. Frank