Method

Stage Pooling Semantic Segmentation Network [SPSSN]
[Anonymous Submission]

Submitted on 12 Feb. 2020 12:15 by
[Anonymous Submission]

Running time:.001 s
Environment:GPU @ >3.5 Ghz (Python)

Method Description:
This research proposes a new light-weight method to solve
pixel-level semantic segmentation. It applied a new stage
pooling method to reuse the paramount features from early
layers at multiple stages at different spatial-
resolutions. This multi-scale approach results a better
segmentation accuracy than many other methods.
Parameters:
learning policy = Poly
Initial learning rate = 0.002
Total number of epochs = 1000
Decay = 5e^-5
momentum = 0.9
Latex Bibtex:

Detailed Results

This page provides detailed results for the method(s) selected. For the first 20 test images, we display the original image, the color-coded result and an error image. The error image contains 4 colors:
red: the pixel has the wrong label and the wrong category
yellow: the pixel has the wrong label but the correct category
green: the pixel has the correct label
black: the groundtruth label is not used for evaluation

Test Set Average

IoU class iIoU class IoU category iIoU category
41.29 14.69 71.91 43.96
This table as LaTeX

Test Image 0

Input Image

Prediction

Error


Test Image 1

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Test Image 2

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Test Image 3

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Test Image 4

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Test Image 5

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Test Image 6

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Test Image 7

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Test Image 8

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Test Image 9

Input Image

Prediction

Error




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