0
$\begingroup$

I am trying to do a single object detection. Since the problem is much simpler than multibox object localization I decided to try using a simple CNN that predicts the object class and its location. Simple neural net for this task in Keras looks like

inpt = Input(shape=input_shape, name='input_image')
h = Conv2D(32, 3, 3, padding='same', activation='relu')(inpt)
h = MaxPooling2D(pool_size=(2, 2))(h)

h = Conv2D(64, 3, 3, padding='same', activation='relu')(h)
out = MaxPooling2D(pool_size=(2, 2))(h)

out = GlobalAveragePooling2D()(out)

coordinates = Dense(4)(out)
classes = Dense(2, activation='softmax')(classes)

model = Model(inputs=inpt, outputs=concatenate([coordinates, classes]))

Here I reduced the problem to detect an object of one type or background - binary classifier.

Now I want to make sure my pipeline is set up correctly so I want to overfit the model with several examples - train. I am able to do it. Next I want to take a bigger net as the base, for example, Mobilenet. And the problem is that I can't overfit it even on one example. Any thoughts?

$\endgroup$
0
$\begingroup$

It turned out the batch normalization layer was the reason. Removing it allowed me to overfit on one example.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.