The KITTI Vision Benchmark Suite

Method

DANet Segmentation [DANet]
[Anonymous Submission]

Submitted on 29 Jun. 2023 05:55 by
[Anonymous Submission]

Running time:		0.05 s
Environment:		GPU @ 2.5 Ghz (Python)

Method Description:

The position attention module selectively
aggregates the feature at each position by a
weighted sum of the features at all positions.
Similar features would be related to each other
regardless of their distances. Meanwhile, the
channel attention module selectively emphasizes
interdependent channel maps by integrating
associated features among all channel maps.

Parameters:

240 epochs、Batchsize are set to 8 for Cityscapes
and 16 for other datasets.

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
59.63	28.56	80.18	55.62

Error

The KITTI Vision Benchmark Suite

A project of Karlsruhe Institute of Technologyand Toyota Technological Institute at Chicago

Method

Detailed Results

Test Set Average

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A project of Karlsruhe Institute of Technology
and Toyota Technological Institute at Chicago