  We propose a new convolution neural network called HarDNet-MSEG for polyp
segmentation. It achieves SOTA in both accuracy and inference speed on five
popular datasets. For Kvasir-SEG, HarDNet-MSEG delivers 0.904 mean Dice running
at 86.7 FPS on a GeForce RTX 2080 Ti GPU. It consists of a backbone and a
decoder. The backbone is a low memory traffic CNN called HarDNet68, which has
been successfully applied to various CV tasks including image classification,
object detection, multi-object tracking and semantic segmentation, etc. The
decoder part is inspired by the Cascaded Partial Decoder, known for fast and
accurate salient object detection. We have evaluated HarDNet-MSEG using those
five popular datasets. The code and all experiment details are available at
Github. https://github.com/james128333/HarDNet-MSEG
