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yeliang2258 7b15f72516 [Backend] Add OCR、Seg、 KeypointDetection、Matting、 ernie-3.0 and adaface models for XPU Deploy (#960) 
* [FlyCV] Bump up FlyCV -> official release 1.0.0

* add seg models for XPU

* add ocr model for XPU

* add matting

* add matting python

* fix infer.cc

* add keypointdetection support for XPU

* Add adaface support for XPU

* add ernie-3.0

* fix doc

Co-authored-by: DefTruth <qiustudent_r@163.com>
Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
2022-12-26 15:02:58 +08:00
..
a311d
[Other] Add FlyCV support for A311D (#928)
2022-12-21 20:00:43 +08:00
android
[Doc] Update OCR README & some dead links (#829)
2022-12-08 10:48:38 +08:00
cpp
[Backend] Add OCR、Seg、 KeypointDetection、Matting、 ernie-3.0 and adaface models for XPU Deploy (#960)
2022-12-26 15:02:58 +08:00
python
[Backend] Add OCR、Seg、 KeypointDetection、Matting、 ernie-3.0 and adaface models for XPU Deploy (#960)
2022-12-26 15:02:58 +08:00
quantize
[Other]Update im.copy() to im in examples (#854)
2022-12-12 09:47:54 +08:00
rknpu2
[RKNPU2] Add Quantized PPHumanSeg (#905)
2022-12-19 20:07:32 +08:00
rv1126
[Other] Add FlyCV support for A311D (#928)
2022-12-21 20:00:43 +08:00
web
[Doc] Fix dead links (#517)
2022-11-07 20:49:41 +08:00
README.md
[Doc] Update PaddleSeg README.md (#733)
2022-11-29 09:45:16 +08:00

README.md

PaddleSeg 模型部署

模型版本说明

目前FastDeploy支持如下模型的部署

【注意】如你部署的为PP-Matting、PP-HumanMatting以及ModNet请参考Matting模型部署

准备PaddleSeg部署模型

PaddleSeg模型导出，请参考其文档说明模型导出

注意

  • PaddleSeg导出的模型包含model.pdmodel、model.pdiparams和deploy.yaml三个文件，FastDeploy会从yaml文件中获取模型在推理时需要的预处理信息

下载预训练模型

为了方便开发者的测试，下面提供了PaddleSeg导出的部分模型

  • without-argmax导出方式为：不指定--input_shape，指定--output_op none
  • with-argmax导出方式为：不指定--input_shape，指定--output_op argmax

开发者可直接下载使用。

模型 参数文件大小 输入Shape mIoU mIoU (flip) mIoU (ms+flip)
Unet-cityscapes-with-argmax | Unet-cityscapes-without-argmax 52MB 1024x512 65.00% 66.02% 66.89%
PP-LiteSeg-B(STDC2)-cityscapes-with-argmax | PP-LiteSeg-B(STDC2)-cityscapes-without-argmax 31MB 1024x512 79.04% 79.52% 79.85%
PP-HumanSegV1-Lite-with-argmax(通用人像分割模型) | PP-HumanSegV1-Lite-without-argmax(通用人像分割模型) 543KB 192x192 86.2% - -
PP-HumanSegV2-Lite-with-argmax(通用人像分割模型) | PP-HumanSegV2-Lite-without-argmax(通用人像分割模型) 12MB 192x192 92.52% - -
PP-HumanSegV2-Mobile-with-argmax(通用人像分割模型) | PP-HumanSegV2-Mobile-without-argmax(通用人像分割模型) 29MB 192x192 93.13% - -
PP-HumanSegV1-Server-with-argmax(通用人像分割模型) | PP-HumanSegV1-Server-without-argmax(通用人像分割模型) 103MB 512x512 96.47% - -
Portait-PP-HumanSegV2-Lite-with-argmax(肖像分割模型) | Portait-PP-HumanSegV2-Lite-without-argmax(肖像分割模型) 3.6M 256x144 96.63% - -
FCN-HRNet-W18-cityscapes-with-argmax | FCN-HRNet-W18-cityscapes-without-argmax(暂时不支持ONNXRuntime的GPU推理) 37MB 1024x512 78.97% 79.49% 79.74%
Deeplabv3-ResNet101-OS8-cityscapes-with-argmax | Deeplabv3-ResNet101-OS8-cityscapes-without-argmax 150MB 1024x512 79.90% 80.22% 80.47%

详细部署文档