Use Case and High-Level Description

This is a segmentation network to classify each pixel into four classes: BG, road, curb, mark.

Example

Specification

Metric	Value
Image size	896x512
GFlops	4.770
MParams	0.184
Source framework	PyTorch*

Accuracy

The quality metrics calculated on 500 images from "Mighty AI" dataset that was converted for four class classification task are:

Label	IOU	ACC
mean	0.844	0.901
BG	0.986	0.994
road	0.954	0.974
curbs	0.727	0.831
marks	0.708	0.806

IOU=TP/(TP+FN+FP)
ACC=TP/GT
TP - number of true positive pixels for given class
FN - number of false negative pixels for given class
FP - number of false positive pixels for given class
GT - number of ground truth pixels for given class

Inputs

A blob with a BGR image in the format: [B, C=3, H=512, W=896], where:

B – batch size
C – number of channels
H – image height
W – image width

Outputs

The output is a blob with the shape [B, C=4, H=512, W=896]. It can be treated as a four-channel feature map, where each channel is a probability of one of the classes: BG, road, curb, mark.

Legal Information

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