def resnet_retinanet(num_classes, backbone='resnet50', inputs=None, modifier=None, **kwargs):
    """ Constructs a retinanet model using a resnet backbone.

    Args
        num_classes: Number of classes to predict.
        backbone: Which backbone to use (one of ('resnet50', 'resnet101', 'resnet152')).
        inputs: The inputs to the network (defaults to a Tensor of shape (None, None, 3)).
        modifier: A function handler which can modify the backbone before using it in retinanet (this can be used to freeze backbone layers for example).

    Returns
        RetinaNet model with a ResNet backbone.
    """
    # choose default input
    if inputs is None:
        if keras.backend.image_data_format() == 'channels_first':
            inputs = keras.layers.Input(shape=(3, None, None))
        else:
            inputs = keras.layers.Input(shape=(None, None, 3))

    # create the resnet backbone
    if backbone == 'resnet50':
        resnet = keras_resnet.models.ResNet50(inputs, include_top=False, freeze_bn=True)
    elif backbone == 'resnet101':
        resnet = keras_resnet.models.ResNet101(inputs, include_top=False, freeze_bn=True)
    elif backbone == 'resnet152':
        resnet = keras_resnet.models.ResNet152(inputs, include_top=False, freeze_bn=True)
    else:
        raise ValueError('Backbone (\'{}\') is invalid.'.format(backbone))

    # invoke modifier if given
    if modifier:
        resnet = modifier(resnet)

    # create the full model
    # resnet.outputs contains 4 layers
    backbone_layers = {
        'C2': resnet.outputs[0],
        'C3': resnet.outputs[1],
        'C4': resnet.outputs[2],
        'C5': resnet.outputs[3]
    }

    return retinanet.retinanet(inputs=inputs, num_classes=num_classes, backbone_layers=backbone_layers, **kwargs)
 Constructs a retinanet model using a resnet backbone.

    Args
        num_classes: Number of classes to predict.
        backbone: Which backbone to use (one of ('resnet50', 'resnet101', 'resnet152')).
        inputs: The inputs to the network (defaults to a Tensor of shape (None, None, 3)).
        modifier: A function handler which can modify the backbone before using it in retinanet (this can be used to freeze backbone layers for example).

    Returns
        RetinaNet model with a ResNet backbone.
    
keras_retinanet/models/resnet.py