On 2024-07-31, marc nicole via Python-list wrote:
> 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
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
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
On 7/30/2024 4:49 PM, marc nicole wrote:
OK, but how's the probability of small_ball greater than others? I can't
find it anyway, what's its value?
It's your code. I wouldn't know. I suppose it's represented somewhere in
all those parameters. You need to understand what those function calls
OK, but how's the probability of small_ball greater than others? I can't
find it anyway, what's its value?
Le mar. 30 juil. 2024 à 21:37, Thomas Passin via Python-list <
python-list@python.org> a écrit :
> On 7/30/2024 2:18 PM, marc nicole via Python-list wrote:
> > Hello all,
> >
> > I want to
On 7/30/2024 2:18 PM, 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
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
