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Semantic Segmentation by ICNet

Implementation of ICNet by Tensorflow


What is ICNet

ICNet is a compressed-PSPNet Based image cascade network, proposed by H. Zhao et al. ICNet for Real-Time Semantic Segmentation on High-Resolution Images. Paper is here . This network is characterized by its runtime and multi-scale input architecture.

arch

Accuracy is measured by Cityscapes data.

This network runs at the speed of 30fps with single GPU. The network is largely composed of simple 3 layers encoding network and 4 layers decoding network.

arch

Different scale of layers are fused in CFF(Cascade Feature Fusion) box.

arch

Dilated Convolution

Dilated convolution encourages a network to dense labeling and enlarge the receptive field.

In tensorflow, it implemented by using tf.nn.astrous

arch

This picture helps understand how dilate convolution works. Depending on dilation value, the filter for convolution become sparse. As a result, it leads to expand receptive fields of layers.

result

Trained with 100 road images. arch

Setup

Framework and Package

  • Python 3.5.2
  • Tensorflow 1.3.0
  • Numpy 1.13.1
  • Scipy 0.19.1

Making Folder

At this directory.

mkdir data

mkdir output

Dataset

Example data is from KITTI road dataset.

Download from here(80Mb).

Download road folder.

Need to put road folder under ./data folder.

Train and Test

1, Make sure you have training data under ./data folder.

2, python main.py

You will have an output example in ./output/post_train_output.png .

Problem

Network architecture should be really similar to the paper. I was supposed to train whole data from KITTI, but it was hard to train whole data due to the lack of hardware resource. I submit codes for training a hundred images.

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