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mobileseg

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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.