I have CNN architecture for object detection ( one object in image ) in KERAS. It has 22 Convolutional layers ( layer includes max pool , LeakyRelu and Batchnorm )
The last layes are following
Basicly i want to output vector that reresents
( probability that is is ObjECT , probability that it isnt , x , y , xmax, ymax )
I am feeding it data that has one object in it and labels
( 1, 0, xmin , ymin , xmax , ymax )
This has around 0.60% accuracy. However the sum of (isOBject + isntObject ) is greater than 1.
[[ 1.02664185 0.34075582 231.69602966 241.59547424 280.6262207 266.35855103]]
Hos is this possible.
Also when i feed network another data that does NOT have object in it and labl them
( 0, 1 , 0, 0, 0, 0 ).
I get 10% accuracy. How can it be? It has more data to learn from and added false data on top of that.
I appreciate all tips and tricks / explanation, basicly all help. Thanks