MMSegmentation is an open source object segmentation toolbox based on PyTorch. It is a part of the OpenMMLab project.
Please refer to get_started.md for installation.
| Model | OnnxRuntime | TensorRT | ncnn | PPLNN | OpenVino | Model config |
|---|---|---|---|---|---|---|
| FCN | Y | Y | Y | Y | Y | config |
| PSPNet* | Y | Y | Y | Y | Y | config |
| DeepLabV3 | Y | Y | Y | Y | Y | config |
| DeepLabV3+ | Y | Y | Y | Y | Y | config |
| Fast-SCNN* | Y | Y | N | Y | Y | config |
| UNet | Y | Y | Y | Y | Y | config |
| ANN* | Y | Y | N | N | N | config |
| APCNet | Y | Y | Y | N | N | config |
| BiSeNetV1 | Y | Y | Y | N | Y | config |
| BiSeNetV2 | Y | Y | Y | N | Y | config |
| CGNet | Y | Y | Y | N | Y | config |
| DMNet | Y | N | N | N | N | config |
| DNLNet | Y | Y | Y | N | Y | config |
| EMANet | Y | Y | N | N | Y | config |
| EncNet | Y | Y | N | N | Y | config |
| ERFNet | Y | Y | Y | N | Y | config |
| FastFCN | Y | Y | Y | N | Y | config |
| GCNet | Y | Y | N | N | N | config |
| ICNet* | Y | Y | N | N | Y | config |
| ISANet* | Y | Y | N | N | Y | config |
| NonLocal Net | Y | Y | Y | N | Y | config |
| OCRNet | Y | Y | Y | N | Y | config |
| PointRend* | Y | Y | N | N | N | config |
| Semantic FPN | Y | Y | Y | N | Y | config |
| STDC | Y | Y | Y | N | Y | config |
| UPerNet* | Y | Y | N | N | N | config |
| DANet | Y | Y | N | N | Y | config |
| Segmenter* | Y | Y | Y | N | Y | config |
| SegFormer* | Y | Y | N | N | Y | config |
| SETR | Y | N | N | N | Y | config |
| CCNet | N | N | N | N | N | config |
| PSANet | N | N | N | N | N | config |
| DPT | N | N | N | N | N | config |
-
Only
wholeinference mode is supported for all mmseg models. -
PSPNet, Fast-SCNN only support static shape, because nn.AdaptiveAvgPool2d is not supported in most of backends dynamically.
-
For models only supporting static shape, you should use the deployment config file of static shape such as
configs/mmseg/segmentation_tensorrt_static-1024x2048.py. -
For users prefer deployed models generate probability feature map, put
codebase_config = dict(with_argmax=False)in deploy configs.