I suppose the meaning of those numbers comes from this line predicts_dict[class_name].append([int(xmin), int(ymin), int(xmax), int(ymax), P[index]]) as well as the yolo inference call. But i was expecting zeros for all classes except smallball. Because the image only shows that, and that a train and a sheep wont have any target position or any probability whatsoever in the image weirdobject.jpg
On Wed, 31 Jul 2024, 00:19 dn via Python-list, <python-list@python.org> wrote: > On 31/07/24 06:18, marc nicole via Python-list wrote: > > Hello all, > > > > I want to predict an object by given as input an image and want to have > my > > model be able to predict the label. I have trained a model using > tensorflow > > based on annotated database where the target object to predict was added > to > > the pretrained model. the code I am using is the following where I set > the > > target object image as input and want to have the prediction output: > > ... > > > > WHile I expect only the dict to contain the small_ball key > > > How's that is possible? where's the prediction output?How to fix the > code? > > > To save us lots of reading and study to be able to help you, please advise: > > 1 what are the meanings of all these numbers? > > > 'sheep': [[233.0, 92.0, 448.0, -103.0, > >> 5.3531270027160645], [167.0, 509.0, 209.0, 101.0, 4.947688579559326], > >> [0.0, 0.0, 448.0, 431.0, 3.393721580505371]] > > 2 (assuming it hasn't) why the dict has not been sorted into a > meaningful order > > 3 how can one tell that the image is more likely to be a sheep than a > train? > > -- > Regards, > =dn > -- > https://mail.python.org/mailman/listinfo/python-list > -- https://mail.python.org/mailman/listinfo/python-list
