SSD(Single Shot MultiBox Detector) is the fastest method in object-detection task (Another detector YOLO, is a little bit slower than SSD). In the source code of MXNET,there is an example for SSD implementation. I test it by using different models: inceptionv3, resnet-50, resnet-101 etc. and find a weird phenomenon: the size .params file generated by resnet-101 is smaller than resnet-50.

Model Size of .params file
resnet-50 119MB
resnet-101 69MB

Since deeper network have larger number of parameters, resnet-101 has smaller file size for parameters seems suspicious.

Reviewing the code of example/ssd/symbol/symbol_factory.py:

    elif network == 'resnet50':
        num_layers = 50
        image_shape = '3,224,224'  # resnet require it as shape check
        network = 'resnet'
        from_layers = ['_plus12', '_plus15', '', '', '', '']
        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()
    elif network == 'resnet101':
        num_layers = 101
        image_shape = '3,224,224'
        network = 'resnet'
        from_layers = ['_plus12', '_plus15', '', '', '', '']
        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()

Why resnet-50 and resnet-101 has the same ‘from_layers’ ? Let’s check these two models:




In resnet-50, the SSD use two layers (as show in red line) to extract features. One from output of stage-3, another from output of stage-4. In resnet-101, it should be the same (as show in blue line), but it incorrectly copy the config code of resnet-50. The correct ‘from_layers’ for resnet-100 is:

from_layers = ['_plus29', '_plus32', '', '', '', '']

This seems like a bug, so I create a pull request to try fixing it.