Re: Predicting an object over an pretrained model is not working

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
>