PaddleSeg/configs/lraspp

• LRASPP
• • Reference
  • Performance
  • • Cityscapes

LRASPP

Reference

Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, et al. "Searching for MobileNetV3." In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 1314-1324. 2019.

Performance

Cityscapes

Model	Backbone	Resolution	Pooling Method	Training Iters	mIoU	mIoU (flip)	mIoU (ms+flip)	Links
LRASPP	MobileNetV3_large_x1_0_os8	1024x512	Global	80000	72.33%	72.63%	73.77%	config | model | log | vdl
LRASPP	MobileNetV3_large_x1_0_os8	1024x512	Large kernel	80000	73.19%	73.40%	74.20%	config | model | log | vdl
LRASPP	MobileNetV3_large_x1_0	1024x512	Global	80000	70.13%	70.43%	72.12%	config | model | log | vdl

Note that:

• The global pooling method refers to the use of a global average pooling layer in the LR-ASPP head, which easily adapts to small-sized input images. In contrast, the large-kernel pooling method uses a 49x49 kernel for average pooling, which is consistent with the design in the original paper.
• MobileNetV3_*_os8 is a variant of MobileNetV3 tailored for semantic segmentation tasks. The output stride is 8, and dilated convolutional layers are used in place of the vanilla convolutional layers in the last two stages.