54 lines
6.8 KiB
Markdown
54 lines
6.8 KiB
Markdown
# mmseg 模型支持列表
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mmseg 是一个基于 PyTorch 的开源对象分割工具箱，也是 [OpenMMLab](https://openmmlab.com/) 项目的一部分。
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## 安装 mmseg
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参照 [get_started.md](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/get_started.md#installation)。
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## 支持列表
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| Model | OnnxRuntime | TensorRT | ncnn | PPLNN | OpenVino | Model config |
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| :--------------------------- | :---------: | :------: | :--: | :---: | :------: | :--------------------------------------------------------------------------------------: |
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| FCN | Y | Y | Y | Y | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/fcn) |
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| PSPNet[\*](#static_shape) | Y | Y | Y | Y | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/pspnet) |
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| DeepLabV3 | Y | Y | Y | Y | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/deeplabv3) |
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| DeepLabV3+ | Y | Y | Y | Y | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/deeplabv3plus) |
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| Fast-SCNN[\*](#static_shape) | Y | Y | N | Y | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/fastscnn) |
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| UNet | Y | Y | Y | Y | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/unet) |
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| ANN[\*](#static_shape) | Y | Y | N | N | N | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/ann) |
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| APCNet | Y | Y | Y | N | N | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/apcnet) |
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| BiSeNetV1 | Y | Y | Y | N | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/bisenetv1) |
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| BiSeNetV2 | Y | Y | Y | N | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/bisenetv2) |
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| CGNet | Y | Y | Y | N | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/cgnet) |
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| DMNet | Y | N | N | N | N | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/dmnet) |
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| DNLNet | Y | Y | Y | N | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/dnlnet) |
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| EMANet | Y | Y | N | N | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/emanet) |
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| EncNet | Y | Y | N | N | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/encnet) |
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| ERFNet | Y | Y | Y | N | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/erfnet) |
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| FastFCN | Y | Y | Y | N | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/fastfcn) |
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| GCNet | Y | Y | N | N | N | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/gcnet) |
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| ICNet[\*](#static_shape) | Y | Y | N | N | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/icnet) |
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| ISANet[\*](#static_shape) | Y | Y | N | N | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/isanet) |
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| NonLocal Net | Y | Y | Y | N | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/nonlocal_net) |
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| OCRNet | Y | Y | Y | N | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/ocrnet) |
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| PointRend[\*](#static_shape) | Y | Y | N | N | N | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/point_rend) |
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| Semantic FPN | Y | Y | Y | N | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/sem_fpn) |
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| STDC | Y | Y | Y | N | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/stdc) |
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| UPerNet[\*](#static_shape) | Y | Y | N | N | N | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/upernet) |
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| DANet | Y | Y | N | N | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/danet) |
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| Segmenter[\*](#static_shape) | Y | Y | Y | N | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/segmenter) |
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| SegFormer[\*](#static_shape) | Y | Y | N | N | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/segformer) |
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| SETR | Y | N | N | N | Y | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/setr) |
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| CCNet | N | N | N | N | N | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/ccnet) |
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| PSANet | N | N | N | N | N | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/psanet) |
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| DPT | N | N | N | N | N | [config](https://github.com/open-mmlab/mmsegmentation/tree/master/configs/dpt) |
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## 注意事项
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- 所有 mmseg 模型仅支持 "whole" 推理模式。
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- <i id=“static_shape”>PSPNet，Fast-SCNN</i> 仅支持静态输入，因为多数推理框架的 [nn.AdaptiveAvgPool2d](https://github.com/open-mmlab/mmsegmentation/blob/97f9670c5a4a2a3b4cfb411bcc26db16b23745f7/mmseg/models/decode_heads/psp_head.py#L38) 不支持动态输入。
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- 对于仅支持静态形状的模型，应使用静态形状的部署配置文件，例如 `configs/mmseg/segmentation_tensorrt_static-1024x2048.py`
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