class chainercv.links.model.segnet.SegNetBasic(n_class=None, pretrained_model=None, initialW=None)¶
SegNet Basic for semantic segmentation.
This is a SegNet  model for semantic segmenation. This is based on
SegNetBasic model that is found here.
When you specify the path of a pretrained chainer model serialized as
a npz file in the constructor, this chain model automatically
initializes all the parameters with it.
When a string in prespecified set is provided, a pretrained model is
loaded from weights distributed on the Internet.
The list of pretrained models supported are as follows:
camvid: Loads weights trained with the train split of CamVid dataset.
Vijay Badrinarayanan, Alex Kendall and Roberto Cipolla “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation.” PAMI, 2017
n_class (int) – The number of classes. If None, it can
be infered if pretrained_model is given.
pretrained_model (str) – The destination of the pretrained
chainer model serialized as a npz file.
If this is one of the strings described
above, it automatically loads weights stored under a directory
where $CHAINER_DATASET_ROOT is set as
$HOME/.chainer/dataset unless you specify another value
by modifying the environment variable.
initialW (callable) – Initializer for convolution layers.