English | 简体中文
PaddleSeg model zoo overview
Model zoo
CNN Series
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