Method

Chroma Unsupervised Domain Adaptation [Chroma UDA]


Submitted on 15 Oct. 2019 11:55 by
Ozgur Erkent (INRIA)

Running time:0.4 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
Initial parameters are trained on Cityscapes
Dataset. No labels from KITTI are used for
adaptation, only RGB images are used.
Parameters:
\lambda=0.1
Latex Bibtex:
@article{erkent:hal-02502457,
TITLE = {{Semantic Segmentation with
Unsupervised Domain Adaptation Under Varying
Weather Conditions for Autonomous Vehicles}},
AUTHOR = {Erkent, Ozgur and Laugier,
Christian},
URL = {https://hal.inria.fr/hal-02502457},
JOURNAL = {{IEEE Robotics and Automation
Letters}},
PUBLISHER = {{IEEE }},
PAGES = {1-8},
YEAR = {2020},
MONTH = Mar,
DOI = {10.1109/LRA.2020.2978666},
PDF = {https://hal.inria.fr/hal-
02502457/file/Erkent-2020-RAL-preprint.pdf},
}

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
60.36 31.70 80.73 61.91
This table as LaTeX

Test Image 0

Input Image

Prediction

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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

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