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airbus-ship-detection-web's Issues

Some review of your classification network 

model = Sequential()

model.add(Convolution2D(32, (3, 3),
                       input_shape=(256, 256, 3),
                       strides = (1,1), 
                       padding = 'same',
                       kernel_initializer='he_normal',
                       activation='relu'))
model.add(BatchNormalization())
model.add(AvgPool2D(pool_size=(2, 2)))


model.add(Convolution2D(32, (3, 3),
                       input_shape=(256, 256, 3),
                       strides = (1,1), 
                       padding = 'same',
                       activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))


model.add(Convolution2D(32, (3, 3),
                       input_shape=(256, 256, 3),
                       strides = (1,1), 
                       padding = 'same',
                       activation='relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))


model.add(Convolution2D(32, (3, 3),
                       input_shape=(256, 256, 3),
                       strides = (1,1), 
                       padding = 'same',
                       activation='relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())

model.add(Dense(units=128, activation='relu', kernel_initializer='normal'))
model.add(Dense(units=1, activation='sigmoid', kernel_initializer='normal'))

model.compile(optimizer='adam',
             loss='binary_crossentropy',
             metrics=['accuracy'])
model.summary()
  1. You want to use a simple network to speed up the classification process. But I think the size of convolutional layer may not increase the parameter number very much. Maybe you can try deeper convolutionala layers, or just simply use a well-build net architecture, like googlenet.
  2. I can't find input_shape parameter in the Keras doc of Conv2d. And strides = (1,1) is the default value. Maybe you don't need to add these two lines in every conv2d layer.
  3. You talk about you are considering about the overfitting. I notice that there are no dropout and input data augmentation in your network, maybe you can try add these features.
  4. The ship detection on Kaggle is a image segmentation task, while you just classifying. Maybe you should start working on U-net. Hope to see you on the Kaggle leaderboard :)

Where can I access your codes?

You have a great illustration of your work and result. But I suppose your project does not contain only one .py file. So if I want to learn your project architecture, could I visit your whole project on Github?

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