Papers › Rethinking BiSeNet For Real-time Semantic Segmentation

Rethinking BiSeNet For Real-time Semantic Segmentation

27 Apr 2021CVPR 2021 1arXiv:2104.13188archive 2025-07-28

Mingyuan Fan, Shenqi Lai, Junshi Huang, Xiaoming Wei, Zhenhua Chai, Junfeng Luo, Xiaolin Wei

BiSeNet has been proved to be a popular two-stream network for real-time segmentation. However, its principle of adding an extra path to encode spatial information is time-consuming, and the backbones borrowed from pretrained tasks, e.g., image classification, may be inefficient for image segmentation due to the deficiency of task-specific design. To handle these problems, we propose a novel and efficient structure named Short-Term Dense Concatenate network (STDC network) by removing structure redundancy. Specifically, we gradually reduce the dimension of feature maps and use the aggregation of them for image representation, which forms the basic module of STDC network. In the decoder, we propose a Detail Aggregation module by integrating the learning of spatial information into low-level layers in single-stream manner. Finally, the low-level features and deep features are fused to predict the final segmentation results. Extensive experiments on Cityscapes and CamVid dataset demonstrate the effectiveness of our method by achieving promising trade-off between segmentation accuracy and inference speed. On Cityscapes, we achieve 71.9% mIoU on the test set with a speed of 250.4 FPS on NVIDIA GTX 1080Ti, which is 45.2% faster than the latest methods, and achieve 76.8% mIoU with 97.0 FPS while inferring on higher resolution images.

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MichaelFan01/STDC-Seg officialmentioned in papermentioned on GitHubpytorch report
Deci-AI/super-gradients mentioned on GitHubpytorch report
pideyi1025/DeepLabV3Plus-RailSem19 mentioned on GitHubpytorchMIT report

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CatBottleneck MichaelFan01/STDC-Seg/nets/stdcnet.py official repository ran fingerprinted MIT (permissive) · 97c83f5a9b50d09c · report
ConvX MichaelFan01/STDC-Seg/nets/stdcnet.py official repository ran · metamorphic tier: invariant fingerprinted MIT (permissive) · bd9e2f16a5c9b881 · report
AddBottleneck MichaelFan01/STDC-Seg/nets/stdcnet.py official repository unverified MIT (permissive) · e352a650699c3703 · report
STDCNet1446 MichaelFan01/STDC-Seg/nets/stdcnet.py official repository unverified MIT (permissive) · 6d18c795468569a2 · report
AttentionRefinementModule zh320/realtime-semantic-segmentation-pytorch/models/stdc.py community (archive-listed) ran · metamorphic tier: invariant fingerprinted Apache-2.0 (permissive) · 651a44aa15b71684 · report
ConvBNAct Deci-AI/super-gradients/src/super_gradients/training/models/segmentation_models/stdc.py community (archive-listed) ran fingerprinted Apache-2.0 (permissive) · 0a4552c5e0d129a9 · report
FeatureFusionModule zh320/realtime-semantic-segmentation-pytorch/models/stdc.py community (archive-listed) ran · metamorphic tier: invariant Apache-2.0 (permissive) · 8ccfe4aa060066bc · report
Residual Deci-AI/super-gradients/src/super_gradients/training/models/segmentation_models/stdc.py community (archive-listed) ran · metamorphic tier: invariant fingerprinted Apache-2.0 (permissive) · 9593929516a67196 · report
STDCModule zh320/realtime-semantic-segmentation-pytorch/models/stdc.py community (archive-listed) ran · metamorphic tier: invariant fingerprinted Apache-2.0 (permissive) · a6375542a388edf0 · report
to_tensor pideyi1025/DeepLabV3Plus-RailSem19/evaluate.py community (archive-listed) ran fingerprinted MIT (permissive) · 233ef2824a5baa1a · report
ConvBNReLU Deci-AI/super-gradients/src/super_gradients/training/models/segmentation_models/stdc.py community (archive-listed) unverified Apache-2.0 (permissive) · 8942f44b7273d2b1 · report
STDC zh320/realtime-semantic-segmentation-pytorch/models/stdc.py community (archive-listed) unverified Apache-2.0 (permissive) · 09fe1398d60fd154 · report
STDCBlock Deci-AI/super-gradients/src/super_gradients/training/models/segmentation_models/stdc.py community (archive-listed) unverified Apache-2.0 (permissive) · be030794ada81ba9 · report
SegHead zh320/realtime-semantic-segmentation-pytorch/models/stdc.py community (archive-listed) unverified Apache-2.0 (permissive) · 548ed9ee6767dd7a · report
SupportsReplaceInputChannels Deci-AI/super-gradients/src/super_gradients/training/models/segmentation_models/stdc.py community (archive-listed) unverified Apache-2.0 (permissive) · 9669048dacdf270a · report
get_preprocessing pideyi1025/DeepLabV3Plus-RailSem19/evaluate.py community (archive-listed) unverified MIT (permissive) · 30208284736b9b4e · report

Tasks

DecoderDichotomous Image SegmentationImage ClassificationImage SegmentationReal-Time Semantic SegmentationSegmentationSemantic Segmentationimage-classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Dichotomous Image Segmentation DIS-TE1 STDC E-measure 0.798 #12 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE1 STDC HCE 249 #12 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE1 STDC MAE 0.090 #12 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE1 STDC S-Measure 0.723 #12 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE1 STDC max F-Measure 0.648 #12 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE1 STDC weighted F-measure 0.562 #12 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE2 STDC E-measure 0.834 #13 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE2 STDC HCE 556 #13 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE2 STDC MAE 0.092 #13 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE2 STDC S-Measure 0.759 #13 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE2 STDC max F-Measure 0.720 #13 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE2 STDC weighted F-measure 0.636 #13 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE3 STDC E-measure 0.855 #16 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE3 STDC HCE 1081 #16 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE3 STDC MAE 0.090 #16 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE3 STDC S-Measure 0.771 #16 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE3 STDC max F-Measure 0.745 #16 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE3 STDC weighted F-measure 0.662 #16 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE4 STDC E-measure 0.841 #14 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE4 STDC HCE 3819 #14 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE4 STDC MAE 0.102 #14 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE4 STDC S-Measure 0.762 #14 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE4 STDC max F-Measure 0.731 #14 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-TE4 STDC weighted F-measure 0.652 #14 of 22 Archive leaderboard report
Dichotomous Image Segmentation DIS-VD STDC E-measure 0.817 #15 of 24 Archive leaderboard report
Dichotomous Image Segmentation DIS-VD STDC HCE 1598 #15 of 24 Archive leaderboard report
Dichotomous Image Segmentation DIS-VD STDC MAE 0.103 #15 of 24 Archive leaderboard report
Dichotomous Image Segmentation DIS-VD STDC S-Measure 0.740 #15 of 24 Archive leaderboard report
Dichotomous Image Segmentation DIS-VD STDC max F-Measure 0.696 #15 of 24 Archive leaderboard report
Dichotomous Image Segmentation DIS-VD STDC weighted F-measure 0.613 #15 of 24 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes test STDC2-75 Frame (fps) 97.0(1080Ti) #8 of 39 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes test STDC2-75 mIoU 76.8% #8 of 39 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes test STDC1-75 Frame (fps) 126.7 #13 of 39 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes test STDC1-75 mIoU 75.3% #13 of 39 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes test STDC2-50 Frame (fps) 188.6 #22 of 39 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes test STDC2-50 mIoU 73.4% #22 of 39 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes test STDC1-50 Frame (fps) 250.4(1080Ti) #25 of 39 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes test STDC1-50 mIoU 71.9% #25 of 39 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes val STDC2-Seg75 Frame (fps) 97 #11 of 24 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes val STDC2-Seg75 mIoU 77% #11 of 24 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes val STDC1-Seg75 Frame (fps) 126.7 #19 of 24 Archive leaderboard report
Real-Time Semantic Segmentation Cityscapes val STDC1-Seg75 mIoU 74.5% #19 of 24 Archive leaderboard report
Semantic Segmentation BDD100K val STDC1 mIoU 52.1(45.8FPS) #17 of 24 Archive leaderboard report
Semantic Segmentation BDD100K val STDC2 mIoU 53.8(33.0FPS) #18 of 24 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Introduced by this paper: STDC

1x1 ConvolutionBatch NormalizationConvolutionReLUSTDC

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