Two kinds of architectures are supported at the moment: the original SegNet
Encoder-Decoder (segnet), and a smaller version of the same (mini), which
can be used for simpler segmentation problems. I suggest to use strided = Truefor faster and more reliable results.
The dataset_name needs to match the data directories you create in your input folder. You can use segnet-32 and segnet-13 to replicate the aforementioned Kendall et al. experiments.
Train and test
Generate your TFRecords using tfrecorder.py. In order to do so, put your PNG
images in a raw folder, as follows: