segnet

SegNet

Preparation

Please create class_weight.npy using calc_weight.py first. Just run:

$ python calc_weight.py

Start training

First, move to this directory (i.e., examples/segnet) and run:

$ python train.py [--gpu <gpu>]

PlotReport extension uses matplotlib. If you got RuntimeError: Invalid DISPLAY variable error on Linux environment, adding an environment variable specification is recommended:

$ MPLBACKEND=Agg python train.py [--gpu <gpu>]

NOTE

• According to the original implementation, the authors performed LR flipping to the input images for data augmentation: https://github.com/alexgkendall/caffe-segnet/blob/segnet-cleaned/src/caffe/layers/dense_image_data_layer.cpp#L168-L175
• Chainer's LRN layer is different from Caffe's one in terms of the meaning of "alpha" argment, so we modified the Chainer's LRN default argment to make it same as Caffe's one: https://github.com/alexgkendall/caffe-segnet/blob/segnet-cleaned/src/caffe/layers/lrn_layer.cpp#L121

Experimental settings

We used the completely same parameters for all settings.

Implementation	Optimizer	Learning rage	Momentum	Weight decay	Model code
Ours	MomentumSGD	0.1	0.9	0.0005	segnet_basic.py
Original	MomentumSGD	0.1	0.9	0.0005	segnet_basic_train.prototxt

Quick Demo

Here is a quick demo using our pretrained weights. The pretrained model is automatically downloaded from the internet.

$ wget https://raw.githubusercontent.com/alexgkendall/SegNet-Tutorial/master/CamVid/test/0001TP_008550.png
$ python demo.py [--gpu <gpu>] [--pretrained-model <model_path>] 0001TP_008550.png

Comparizon with the paper results

Implementation	Global accuracy	Class accuracy	mean IoU
Ours	82.7 %	67.1 %	49.4 %
Original	82.8 %	62.3%	46.3 %

The evaluation can be conducted using chainercv/examples/semantic_segmentation/eval_semantic_segmentation.py.

Reference

1. Vijay Badrinarayanan, Alex Kendall and Roberto Cipolla "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation." PAMI, 2017.
2. Vijay Badrinarayanan, Ankur Handa and Roberto Cipolla "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling." arXiv preprint arXiv:1505.07293, 2015.
3. Original implementation