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PaddleSeg model zoo overview

Model zoo

CNN Series

Model\Backbone Network ResNet50 ResNet101 HRNetw18 HRNetw48
ANN ✔ ✔
BiSeNetv2 - - - -
DANet ✔ ✔
Deeplabv3 ✔ ✔
Deeplabv3P ✔ ✔
Fast-SCNN - - - -
FCN ✔ ✔
GCNet ✔ ✔
GSCNN ✔ ✔
HarDNet - - - -
OCRNet ✔ ✔
PSPNet ✔ ✔
U-Net - - - -
U2-Net - - - -
Att U-Net - - - -
U-Net++ - - - -
U-Net3+ - - - -
DecoupledSegNet ✔ ✔
EMANet ✔ ✔ - -
ISANet ✔ ✔ - -
DNLNet ✔ ✔ - -
SFNet ✔ - - -
PP-HumanSeg-Lite - - - -
PortraitNet - - - -
STDC - - - -
GINet ✔ ✔ - -
PointRend ✔ ✔ - -
SegNet - - - -
ESPNetV2 - - - -
HRNetW48Contrast - - - ✔
DMNet - ✔ - -
ESPNetV1 - - - -
ENCNet - ✔ - -
PFPNNet - ✔ - -
FastFCN ✔ - - -
BiSeNetV1 - - - -
ENet - - - -
CCNet - ✔ - -
DDRNet - - - -
GloRe ✔ - - -
PP-LiteSeg - - - -

Transformer series

Model zoo benchmark

Based on the Cityscapes dataset, PaddleSeg supports 22+ series of segmentation algorithms and corresponding 30+ image segmentation pre-training models. The performance is evaluated as follows.

Test environment:

  • GPU: Tesla V100 16GB
  • CPU: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
  • CUDA: 10.2
  • cuDNN: 7.6
  • Paddle: 2.1.3
  • PaddleSeg: 2.3

Test method:

  • Single GPU, Batch size is 1, the running time is pure model prediction time, and the predicted image size is 1024x512.
  • Use Paddle Inference's Python API to test the model after export.
  • Inference time is the result of averaging predictions using 100 images in the CityScapes dataset.
  • Some algorithms have only tested performance under the configuration that achieves the highest segmentation accuracy.

Accuracy vs Speed

Accuracy vs FLOPs

Accuracy vs Params

Summary table

Model Backbone mIoU Flops(G) Params(M) Inference Time(ms) Preprocess Time(ms) Postprocess Time(ms)
BiSeNetv2 - 73.19% 16.14 2.33 16.00 167.45 0.013
Fast-SCNN - 69.31% 2.04 1.44 10.43 161.52 0.012
HarDNet - 79.03% 35.40 4.13 21.19 164.36 0.013
U-Net - 65.00% 253.75 13.41 29.11 137.75 0.012
SegFormer_B0 - 76.73% 13.63 3.72 15.66 152.60 0.017
SegFormer_B1 - 78.35% 26.55 13.68 21.48 152.40 0.017
STDC1-Seg50 STDC1 74.74% 24.83 8.29 9.10 153.01 0.016
STDC2-Seg50 STDC2 77.60% 38.05 12.33 10.88 152.64 0.015
ANN ResNet101 79.50% 564.43 67.70 94.91 143.35 0.013
DANet ResNet50 80.27% 398.48 47.52 95.08 134.78 0.015
Deeplabv3 ResNet101_OS8 80.85% 481.00 58.17 114 141.65 0.014
Deeplabv3P ResNet50_OS8 81.10% 228.44 26.79 69.78 147.24 0.016
FCN HRNet_W48 80.70% 187.50 65.94 45.46 130.58 0.012
GCNet ResNet101_OS8 81.01% 570.74 68.73 90.28 119.38 0.013
OCRNet HRNet_W48 82.15% 324.66 70.47 61.88 138.48 0.014
PSPNet ResNet101_OS8 80.48% 686.89 86.97 115.93 115.94 0.012
DecoupledSegNet ResNet50_OS8 81.26% 395.10 41.71 66.89 136.28 0.013
EMANet ResNet101_OS8 80.00% 512.18 61.45 80.05 140.47 0.013
ISANet ResNet101_OS8 80.10% 474.13 56.81 91.72 129.12 0.012
DNLNet ResNet101_OS8 81.03% 575.04 69.13 97.81 138.95 0.014
SFNet ResNet18_OS8 78.72% 136.80 13.81 69.51 131.67 0.015
SFNet ResNet50_OS8 81.49% 394.37 42.03 121.35 160.45 0.013
PointRend ResNet50_OS8 76.54% 363.17 28.18 70.35 157.24 0.016
SegFormer_B2 - 81.60% 113.71 27.36 47.08 155.45 0.016
SegFormer_B3 - 82.47% 142.97 47.24 62.70 154.68 0.017
SegFormer_B4 - 82.38% 171.05 64.01 73.26 151.11 0.017
SegFormer_B5 - 82.58% 199.68 84.61 84.34 147.92 0.016
SETR-Naive Vision Transformer 77.29% 620.94 303.37 201.26 145.76 0.016
SETR-PUP Vision Transformer 78.08% 727.46 307.24 212.22 147.05 0.016
SETR-MLA Vision Transformer 76.52% 633.88 307.05 204.87 145.87 0.015

How to add new models to Benchmark

Performance statistics

Set up a test environment according to the above configuration, and test according to the requirements of the test method. Among them, Inference Time(ms), Preprocess Time(ms), Postprocess Time(ms) can be tested through PaddleSeg Inference Deployment Tutorial, and enable the --benchmark parameter for inference.

Chart drawing

Update the obtained performance data to the table. The performance comparison chart drawing code is located in PaddleSeg/tools/plot_model_performance.py, supplement the performance data of the model in set_model_info(), run

python plot_model_performance.py

All performance comparison charts are available