Papers › PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model

PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model

6 Apr 2022arXiv:2204.02681archive 2025-07-28

Juncai Peng, Yi Liu, Shiyu Tang, Yuying Hao, Lutao Chu, Guowei Chen, Zewu Wu, Zeyu Chen, Zhiliang Yu, Yuning Du, Qingqing Dang, Baohua Lai, Qiwen Liu, Xiaoguang Hu, dianhai yu, Yanjun Ma

Real-world applications have high demands for semantic segmentation methods. Although semantic segmentation has made remarkable leap-forwards with deep learning, the performance of real-time methods is not satisfactory. In this work, we propose PP-LiteSeg, a novel lightweight model for the real-time semantic segmentation task. Specifically, we present a Flexible and Lightweight Decoder (FLD) to reduce computation overhead of previous decoder. To strengthen feature representations, we propose a Unified Attention Fusion Module (UAFM), which takes advantage of spatial and channel attention to produce a weight and then fuses the input features with the weight. Moreover, a Simple Pyramid Pooling Module (SPPM) is proposed to aggregate global context with low computation cost. Extensive evaluations demonstrate that PP-LiteSeg achieves a superior trade-off between accuracy and speed compared to other methods. On the Cityscapes test set, PP-LiteSeg achieves 72.0% mIoU/273.6 FPS and 77.5% mIoU/102.6 FPS on NVIDIA GTX 1080Ti. Source code and models are available at PaddleSeg: https://github.com/PaddlePaddle/PaddleSeg.

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Code

PaddlePaddle/PaddleSeg officialmentioned in papermentioned on GitHubpaddle report
Deci-AI/super-gradients mentioned on GitHubpytorch report

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Tasks

DecoderReal-Time Semantic SegmentationSegmentationSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Real-Time Semantic Segmentation CamVid PP-LiteSeg-B Frame (fps) 154.8 #12 of 29 Archive leaderboard report
Real-Time Semantic Segmentation CamVid PP-LiteSeg-B mIoU 75 #12 of 29 Archive leaderboard report
Real-Time Semantic Segmentation CamVid PP-LiteSeg-T Frame (fps) 222.3 #16 of 29 Archive leaderboard report
Real-Time Semantic Segmentation CamVid PP-LiteSeg-T mIoU 73.3 #16 of 29 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes test PP-LiteSeg-B2 Frame (fps) 102.6(1080Ti) #6 of 39 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes test PP-LiteSeg-B2 mIoU 77.5% #6 of 39 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes test PP-LiteSeg-T2 Frame (fps) 143.6(1080Ti) #16 of 39 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes test PP-LiteSeg-T2 mIoU 74.9% #16 of 39 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes test PP-LiteSeg-B1 Frame (fps) 195.3(1080Ti) #20 of 39 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes test PP-LiteSeg-B1 mIoU 73.9% #20 of 39 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes test PP-LiteSeg-T1 Frame (fps) 273.6(1080Ti) #24 of 39 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes test PP-LiteSeg-T1 mIoU 72.0% #24 of 39 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes val PP-LiteSeg-B2 mIoU 78.2 #9 of 24 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes val PP-LiteSeg-T2 mIoU 76 #14 of 24 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes val PP-LiteSeg-B1 mIoU 75.3 #17 of 24 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes val PP-LiteSeg-T1 mIoU 73.1 #22 of 24 Archive leaderboard report

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Methods

Average PoolingBatch NormalizationConvolutionPyramid Pooling ModuleReLUSPEED

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