Hi Kevin,
I understand that you would like to use the same neural network for classification and regression tasks.
Regarding your first question, though it is convenient to create such a network which performs conditional regression step, it is not feasible to do so as the model would be trained to perform classification and not regression. Simply adding a regression layer after classification won’t work as the layer weights would also need to be different for classification and regression tasks.
It is advisable to first train your model for classification of images and then train a separate model for regression. After that you can use a conditional statement to decide when the regression model should be used.
Regarding your second question, you need to provide the coordinates for all the images even if some images do not contain particles. The reason for this is however well you train the model it is bound to have some error and thus can classify an image not containing particles as an image containing particles. To account for this, you could set the coordinates of the images not containing a particle to infinity. This would ensure that the model does not break. Also, it would generate a huge error term in the regression model from which you could flag the error. Just ensure that you do not train the regression model with such samples as it will affect the layer weights adversely.
I hope this helps in your task.
Regards,
Avadhoot.
