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English | 简体中文
PaddleSeg 模型部署
模型版本说明
目前FastDeploy使用RKNPU2推理PPSeg支持如下模型的部署:
| 模型 | 参数文件大小 | 输入Shape | mIoU | mIoU (flip) | mIoU (ms+flip) |
|---|---|---|---|---|---|
| Unet-cityscapes | 52MB | 1024x512 | 65.00% | 66.02% | 66.89% |
| PP-LiteSeg-T(STDC1)-cityscapes | 31MB | 1024x512 | 77.04% | 77.73% | 77.46% |
| PP-HumanSegV1-Lite(通用人像分割模型) | 543KB | 192x192 | 86.2% | - | - |
| PP-HumanSegV2-Lite(通用人像分割模型) | 12MB | 192x192 | 92.52% | - | - |
| PP-HumanSegV2-Mobile(通用人像分割模型) | 29MB | 192x192 | 93.13% | - | - |
| PP-HumanSegV1-Server(通用人像分割模型) | 103MB | 512x512 | 96.47% | - | - |
| Portait-PP-HumanSegV2_Lite(肖像分割模型) | 3.6M | 256x144 | 96.63% | - | - |
| FCN-HRNet-W18-cityscapes | 37MB | 1024x512 | 78.97% | 79.49% | 79.74% |
| Deeplabv3-ResNet101-OS8-cityscapes | 150MB | 1024x512 | 79.90% | 80.22% | 80.47% |
准备PaddleSeg部署模型以及转换模型
RKNPU部署模型前需要将Paddle模型转换成RKNN模型，具体步骤如下:
- Paddle动态图模型转换为ONNX模型，请参考PaddleSeg模型导出说明
- ONNX模型转换RKNN模型的过程，请参考转换文档进行转换。