ShuffleSeg

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Implementations

Real-time Semantic Segmentation Comparative Study

Semantic segmentation benefits robotics related applications especially autonomous driving. Most of the research on semantic segmentation is only on increasing the accuracy of segmentation models with little attention to computationally efficient solutions. The few work conducted in this direction does not provide principled methods to evaluate the     different design choices for segmentation. In RTSeg, we address this gap by presenting a real-time semantic segmentation benchmarking framework with a decoupled design for feature extraction and decoding methods. The code and the experimental results are presented on the CityScapes dataset for urban scenes.

Model: https://github.com/MSiam/TFSegmentation/blob/master/models/encoders/resnet_18.py

Github: https://github.com/MSiam/TFSegmentation

segmentationshuffleseg
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