apps/FastDeploy
1
0
Fork 0
mirror of https://github.com/PaddlePaddle/FastDeploy.git synced 2025-10-16 13:41:30 +08:00
Code Issues Actions 6 Packages Projects Releases Wiki Activity
Files
9920cc84a700b1825c8897b16e08804934dea896
FastDeploy/examples/vision/segmentation/paddleseg
History
yunyaoXYY d49160252b [Other] Improve examples and readme for Ascend deployment (#1052) 
* Add Huawei Ascend NPU deploy through PaddleLite CANN

* Add NNAdapter interface for paddlelite

* Modify Huawei Ascend Cmake

* Update way for compiling Huawei Ascend NPU deployment

* remove UseLiteBackend in UseCANN

* Support compile python whlee

* Change names of nnadapter API

* Add nnadapter pybind and remove useless API

* Support Python deployment on Huawei Ascend NPU

* Add models suppor for ascend

* Add PPOCR rec reszie for ascend

* fix conflict for ascend

* Rename CANN to Ascend

* Rename CANN to Ascend

* Improve ascend

* fix ascend bug

* improve ascend docs

* improve ascend docs

* improve ascend docs

* Improve Ascend

* Improve Ascend

* Move ascend python demo

* Imporve ascend

* Improve ascend

* Improve ascend

* Improve ascend

* Improve ascend

* Imporve ascend

* Imporve ascend

* Improve ascend

* acc eval script

* acc eval

* remove acc_eval from branch huawei

* Add detection and segmentation examples for Ascend deployment

* Add detection and segmentation examples for Ascend deployment

* Add PPOCR example for ascend deploy

* Imporve paddle lite compiliation

* Add FlyCV doc

* Add FlyCV doc

* Add FlyCV doc

* Imporve Ascend docs

* Imporve Ascend docs

* Improve PPOCR example
2023-01-04 16:18:38 +08:00
..
a311d
[Doc] Update YOLOv5 doc for TIMVX NPU (#1041)
2023-01-04 10:32:15 +08:00
android
[Doc] Update OCR README & some dead links (#829)
2022-12-08 10:48:38 +08:00
cpp
[Other] Improve examples and readme for Ascend deployment (#1052)
2023-01-04 16:18:38 +08:00
python
[Other] Add detection, segmentation and OCR examples for Ascend deploy. (#983)
2023-01-04 10:01:23 +08:00
quantize
[Other]Update im.copy() to im in examples (#854)
2022-12-12 09:47:54 +08:00
rknpu2
[Model] Update PPSeg Preprocess (#1007)
2022-12-29 21:14:39 +08:00
rv1126
[Doc] Update YOLOv5 doc for TIMVX NPU (#1041)
2023-01-04 10:32:15 +08:00
sophgo
[Backend] add sophgo backend (#1015)
2023-01-04 15:49:17 +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