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PaddleSeg provides 45+ semantic segmentation models, 150+ well-trained models, 10+ backbones.
In PaddleSeg/configs, we provide the config files and readme.md for all models on common dataset, e.g., PP-LiteSeg.
Besides, the readme.md file introduces the origin paper, the performance and the trained weights.
Some common models are as follows.
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