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| 模型\骨干网络 | 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 | - | - | - | - |
基于Cityscapes数据集，PaddleSeg支持22+系列分割算法以及对应的30+个图像分割预训练模型，性能评估如下。
测试环境：
- 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
测试方法:
- 单GPU，Batch size为1，运行耗时为纯模型预测时间，预测图片尺寸为1024x512。
- 模型导出后使用Paddle Inference的Python API测试。
- 推理时间是使用CityScapes数据集中的100张图像进行预测取平均值的结果。
- 部分算法只测试了取得最高分割精度的配置下的模型性能。
| 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 |