How to compose SSD network on top of mainstream classification networks

1. Have the base network ready in this directory as name.py, such as inceptionv3.py.
2. Add configuration to symbol_factory.py, an example would be:

if network == 'vgg16_reduced':
    if data_shape >= 448:
        from_layers = ['relu4_3', 'relu7', '', '', '', '', '']
        num_filters = [512, -1, 512, 256, 256, 256, 256]
        strides = [-1, -1, 2, 2, 2, 2, 1]
        pads = [-1, -1, 1, 1, 1, 1, 1]
        sizes = [[.07, .1025], [.15,.2121], [.3, .3674], [.45, .5196], [.6, .6708], \
            [.75, .8216], [.9, .9721]]
        ratios = [[1,2,.5], [1,2,.5,3,1./3], [1,2,.5,3,1./3], [1,2,.5,3,1./3], \
            [1,2,.5,3,1./3], [1,2,.5], [1,2,.5]]
        normalizations = [20, -1, -1, -1, -1, -1, -1]
        steps = [] if data_shape != 512 else [x / 512.0 for x in
            [8, 16, 32, 64, 128, 256, 512]]
    else:
        from_layers = ['relu4_3', 'relu7', '', '', '', '']
        num_filters = [512, -1, 512, 256, 256, 256]
        strides = [-1, -1, 2, 2, 1, 1]
        pads = [-1, -1, 1, 1, 0, 0]
        sizes = [[.1, .141], [.2,.272], [.37, .447], [.54, .619], [.71, .79], [.88, .961]]
        ratios = [[1,2,.5], [1,2,.5,3,1./3], [1,2,.5,3,1./3], [1,2,.5,3,1./3], \
            [1,2,.5], [1,2,.5]]
        normalizations = [20, -1, -1, -1, -1, -1]
        steps = [] if data_shape != 300 else [x / 300.0 for x in [8, 16, 32, 64, 100, 300]]
    return locals()
elif network == 'inceptionv3':
    from_layers = ['ch_concat_mixed_7_chconcat', 'ch_concat_mixed_10_chconcat', '', '', '', '']
    num_filters = [-1, -1, 512, 256, 256, 128]
    strides = [-1, -1, 2, 2, 2, 2]
    pads = [-1, -1, 1, 1, 1, 1]
    sizes = [[.1, .141], [.2,.272], [.37, .447], [.54, .619], [.71, .79], [.88, .961]]
    ratios = [[1,2,.5], [1,2,.5,3,1./3], [1,2,.5,3,1./3], [1,2,.5,3,1./3], \
        [1,2,.5], [1,2,.5]]
    normalizations = -1
    steps = []
    return locals()

Here from_layers indicate the feature layer you would like to extract from the base network. '' indicate that we want add extra new layers on top of the last feature layer, and the number of filters must be specified in num_filters. Similarly, strides and pads are required to compose these new layers. sizes and ratios are the parameters controlling the anchor generation algorithm. normalizations is used to normalize and rescale feature if not -1. steps: optional, used to calculate the anchor sliding steps.

3. Train or test with arguments --network name --data-shape xxx --pretrained pretrained_model