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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

model.add(Flatten())
model.add(Dense(6))

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

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You need to use appropriate activations. If you were using softmax for those two components, they'd be constrained to sum to one. LeakyReLU not only doesn't impose this constraint: it can output any real value, so it isn't even constrained to [0,1] for those components individually.

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  • $\begingroup$ Can i use one activations for first 2 parameters, and second for other 4? $\endgroup$ – trolkura Mar 23 '18 at 10:12
  • $\begingroup$ Of course you can. Use the functional API instead of sequential, and treat it as two separate output layers: one with two softmax nodes, and one with 4 LeakyReLU or whatever. $\endgroup$ – David Marx Mar 23 '18 at 10:15
  • $\begingroup$ Please. Would like be willing go demonstrate simple example? $\endgroup$ – trolkura Mar 23 '18 at 10:16
  • $\begingroup$ This is the basic idea: datascience.stackexchange.com/a/23676/4683 $\endgroup$ – David Marx Mar 23 '18 at 10:18

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