paddle/configs/mobileseg

• MobileSeg
• • Reference
  • Performance
  • • Cityscapes
  • Inference Speed

English | 简体中文

MobileSeg

These semantic segmentation models are designed for mobile and edge devices.

MobileSeg models adopt encoder-decoder architecture and use lightweight models as encoder.

Reference

Sandler, Mark, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen. "Mobilenetv2: Inverted residuals and linear bottlenecks." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4510-4520. 2018.

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, pp. 1314-1324. 2019.

Ma, Ningning, Xiangyu Zhang, Hai-Tao Zheng, and Jian Sun. "Shufflenet v2: Practical guidelines for efficient cnn architecture design." In Proceedings of the European conference on computer vision (ECCV), pp. 116-131. 2018.

Yu, Changqian, Bin Xiao, Changxin Gao, Lu Yuan, Lei Zhang, Nong Sang, and Jingdong Wang. "Lite-hrnet: A lightweight high-resolution network." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10440-10450. 2021.

Han, Kai, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, and Chang Xu. "Ghostnet: More features from cheap operations." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1580-1589. 2020.

Performance

Cityscapes

Model	Backbone	Resolution	Training Iters	mIoU	mIoU (flip)	mIoU (ms+flip)	Links
MobileSeg	MobileNetV2	1024x512	80000	73.94%	74.32%	75.38%	model | log | vdl
MobileSeg	MobileNetV3_large_x1_0	1024x512	80000	73.47%	73.72%	74.72%	model | log | vdl
MobileSeg	Lite_HRNet_18	1024x512	80000	70.75%	71.62%	72.53%	model | log | vdl
MobileSeg	ShuffleNetV2_x1_0	1024x512	80000	69.46%	70.00%	70.90%	model | log | vdl
MobileSeg	GhostNet_x1_0	1024x512	80000	71.88%	72.22%	73.08%	model | log | vdl

Inference Speed

Model	Backbone	V100 TRT Inference Speed(FPS)	Snapdragon 855 Inference Speed(FPS)
MobileSeg	MobileNetV2	67.57	27.01
MobileSeg	MobileNetV3_large_x1_0	67.39	32.90
MobileSeg	Lite_HRNet_18	10.5	13.05
MobileSeg	ShuffleNetV2_x1_0	37.09	39.61
MobileSeg	GhostNet_x1_0	35.58	38.74

Note that:

• Test the inference speed on Nvidia GPU V100: use PaddleInference Python API, enable TensorRT, the data type is FP32, the dimension of input is 1x3x1024x2048.
• Test the inference speed on Snapdragon 855: use PaddleLite CPP API, 1 thread, the dimension of input is 1x3x256x256.