Modify external models docs (PaddlePaddle#118)

* first commit for yolov7

* pybind for yolov7

* CPP README.md

* CPP README.md

* modified yolov7.cc

* README.md

* python file modify

* delete license in fastdeploy/

* repush the conflict part

* README.md modified

* README.md modified

* file path modified

* file path modified

* file path modified

* file path modified

* file path modified

* README modified

* README modified

* move some helpers to private

* add examples for yolov7

* api.md modified

* api.md modified

* api.md modified

* YOLOv7

* yolov7 release link

* yolov7 release link

* yolov7 release link

* copyright

* change some helpers to private

* change variables to const and fix documents.

* gitignore

* Transfer some funtions to private member of class

* Transfer some funtions to private member of class

* Merge from develop (#9)

* Fix compile problem in different python version (#26)

* fix some usage problem in linux

* Fix compile problem

Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>

* Add PaddleDetetion/PPYOLOE model support (#22)

* add ppdet/ppyoloe

* Add demo code and documents

* add convert processor to vision (#27)

* update .gitignore

* Added checking for cmake include dir

* fixed missing trt_backend option bug when init from trt

* remove un-need data layout and add pre-check for dtype

* changed RGB2BRG to BGR2RGB in ppcls model

* add model_zoo yolov6 c++/python demo

* fixed CMakeLists.txt typos

* update yolov6 cpp/README.md

* add yolox c++/pybind and model_zoo demo

* move some helpers to private

* fixed CMakeLists.txt typos

* add normalize with alpha and beta

* add version notes for yolov5/yolov6/yolox

* add copyright to yolov5.cc

* revert normalize

* fixed some bugs in yolox

* fixed examples/CMakeLists.txt to avoid conflicts

* add convert processor to vision

* format examples/CMakeLists summary

* Fix bug while the inference result is empty with YOLOv5 (#29)

* Add multi-label function for yolov5

* Update README.md

Update doc

* Update fastdeploy_runtime.cc

fix variable option.trt_max_shape wrong name

* Update runtime_option.md

Update resnet model dynamic shape setting name from images to x

* Fix bug when inference result boxes are empty

* Delete detection.py

Co-authored-by: Jason <jiangjiajun@baidu.com>
Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>
Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
Co-authored-by: huangjianhui <852142024@qq.com>

* first commit for yolor

* for merge

* Develop (#11)

* Fix compile problem in different python version (#26)

* fix some usage problem in linux

* Fix compile problem

Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>

* Add PaddleDetetion/PPYOLOE model support (#22)

* add ppdet/ppyoloe

* Add demo code and documents

* add convert processor to vision (#27)

* update .gitignore

* Added checking for cmake include dir

* fixed missing trt_backend option bug when init from trt

* remove un-need data layout and add pre-check for dtype

* changed RGB2BRG to BGR2RGB in ppcls model

* add model_zoo yolov6 c++/python demo

* fixed CMakeLists.txt typos

* update yolov6 cpp/README.md

* add yolox c++/pybind and model_zoo demo

* move some helpers to private

* fixed CMakeLists.txt typos

* add normalize with alpha and beta

* add version notes for yolov5/yolov6/yolox

* add copyright to yolov5.cc

* revert normalize

* fixed some bugs in yolox

* fixed examples/CMakeLists.txt to avoid conflicts

* add convert processor to vision

* format examples/CMakeLists summary

* Fix bug while the inference result is empty with YOLOv5 (#29)

* Add multi-label function for yolov5

* Update README.md

Update doc

* Update fastdeploy_runtime.cc

fix variable option.trt_max_shape wrong name

* Update runtime_option.md

Update resnet model dynamic shape setting name from images to x

* Fix bug when inference result boxes are empty

* Delete detection.py

Co-authored-by: Jason <jiangjiajun@baidu.com>
Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>
Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
Co-authored-by: huangjianhui <852142024@qq.com>

* Yolor (#16)

* Develop (#11) (#12)

* Fix compile problem in different python version (#26)

* fix some usage problem in linux

* Fix compile problem

Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>

* Add PaddleDetetion/PPYOLOE model support (#22)

* add ppdet/ppyoloe

* Add demo code and documents

* add convert processor to vision (#27)

* update .gitignore

* Added checking for cmake include dir

* fixed missing trt_backend option bug when init from trt

* remove un-need data layout and add pre-check for dtype

* changed RGB2BRG to BGR2RGB in ppcls model

* add model_zoo yolov6 c++/python demo

* fixed CMakeLists.txt typos

* update yolov6 cpp/README.md

* add yolox c++/pybind and model_zoo demo

* move some helpers to private

* fixed CMakeLists.txt typos

* add normalize with alpha and beta

* add version notes for yolov5/yolov6/yolox

* add copyright to yolov5.cc

* revert normalize

* fixed some bugs in yolox

* fixed examples/CMakeLists.txt to avoid conflicts

* add convert processor to vision

* format examples/CMakeLists summary

* Fix bug while the inference result is empty with YOLOv5 (#29)

* Add multi-label function for yolov5

* Update README.md

Update doc

* Update fastdeploy_runtime.cc

fix variable option.trt_max_shape wrong name

* Update runtime_option.md

Update resnet model dynamic shape setting name from images to x

* Fix bug when inference result boxes are empty

* Delete detection.py

Co-authored-by: Jason <jiangjiajun@baidu.com>
Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>
Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
Co-authored-by: huangjianhui <852142024@qq.com>

Co-authored-by: Jason <jiangjiajun@baidu.com>
Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>
Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
Co-authored-by: huangjianhui <852142024@qq.com>

* Develop (#13)

* Fix compile problem in different python version (#26)

* fix some usage problem in linux

* Fix compile problem

Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>

* Add PaddleDetetion/PPYOLOE model support (#22)

* add ppdet/ppyoloe

* Add demo code and documents

* add convert processor to vision (#27)

* update .gitignore

* Added checking for cmake include dir

* fixed missing trt_backend option bug when init from trt

* remove un-need data layout and add pre-check for dtype

* changed RGB2BRG to BGR2RGB in ppcls model

* add model_zoo yolov6 c++/python demo

* fixed CMakeLists.txt typos

* update yolov6 cpp/README.md

* add yolox c++/pybind and model_zoo demo

* move some helpers to private

* fixed CMakeLists.txt typos

* add normalize with alpha and beta

* add version notes for yolov5/yolov6/yolox

* add copyright to yolov5.cc

* revert normalize

* fixed some bugs in yolox

* fixed examples/CMakeLists.txt to avoid conflicts

* add convert processor to vision

* format examples/CMakeLists summary

* Fix bug while the inference result is empty with YOLOv5 (#29)

* Add multi-label function for yolov5

* Update README.md

Update doc

* Update fastdeploy_runtime.cc

fix variable option.trt_max_shape wrong name

* Update runtime_option.md

Update resnet model dynamic shape setting name from images to x

* Fix bug when inference result boxes are empty

* Delete detection.py

Co-authored-by: Jason <jiangjiajun@baidu.com>
Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>
Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
Co-authored-by: huangjianhui <852142024@qq.com>

* documents

* documents

* documents

* documents

* documents

* documents

* documents

* documents

* documents

* documents

* documents

* documents

* Develop (#14)

* Fix compile problem in different python version (#26)

* fix some usage problem in linux

* Fix compile problem

Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>

* Add PaddleDetetion/PPYOLOE model support (#22)

* add ppdet/ppyoloe

* Add demo code and documents

* add convert processor to vision (#27)

* update .gitignore

* Added checking for cmake include dir

* fixed missing trt_backend option bug when init from trt

* remove un-need data layout and add pre-check for dtype

* changed RGB2BRG to BGR2RGB in ppcls model

* add model_zoo yolov6 c++/python demo

* fixed CMakeLists.txt typos

* update yolov6 cpp/README.md

* add yolox c++/pybind and model_zoo demo

* move some helpers to private

* fixed CMakeLists.txt typos

* add normalize with alpha and beta

* add version notes for yolov5/yolov6/yolox

* add copyright to yolov5.cc

* revert normalize

* fixed some bugs in yolox

* fixed examples/CMakeLists.txt to avoid conflicts

* add convert processor to vision

* format examples/CMakeLists summary

* Fix bug while the inference result is empty with YOLOv5 (#29)

* Add multi-label function for yolov5

* Update README.md

Update doc

* Update fastdeploy_runtime.cc

fix variable option.trt_max_shape wrong name

* Update runtime_option.md

Update resnet model dynamic shape setting name from images to x

* Fix bug when inference result boxes are empty

* Delete detection.py

Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>
Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
Co-authored-by: huangjianhui <852142024@qq.com>

Co-authored-by: Jason <jiangjiajun@baidu.com>
Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>
Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
Co-authored-by: huangjianhui <852142024@qq.com>
Co-authored-by: Jason <928090362@qq.com>

* add is_dynamic for YOLO series (#22)

* modify api docs

* modify api docs

* modify yolor docs

* modify external docs

* modify external docs

Co-authored-by: Jason <jiangjiajun@baidu.com>
Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com>
Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>
Co-authored-by: huangjianhui <852142024@qq.com>
Co-authored-by: Jason <928090362@qq.com>

examples/vision/detection/README.md

@@ -14,7 +14,7 @@ FastDeploy目前支持如下目标检测模型部署
 | [RangiLyu/NanoDetPlus](./nanodet_plus) | NanoDetPlus 系列模型 | ONNX | [Release/v1.0.0-alpha-1](https://github.com/RangiLyu/nanodet/tree/v1.0.0-alpha-1) |
 | [ultralytics/YOLOv5](./yolov5) | YOLOv5 系列模型 | ONNX | [Release/v6.0](https://github.com/ultralytics/yolov5/tree/v6.0) |
 | [ppogg/YOLOv5-Lite](./yolov5lite) | YOLOv5-Lite 系列模型 | ONNX | [Release/v1.4](https://github.com/ppogg/YOLOv5-Lite/releases/tag/v1.4) |
-| [meituan/YOLOv6](./yolov6) | YOLOv6 系列模型 | ONNX | [Release/0.1.0](https://github.com/meituan/YOLOv6/releases/download/0.1.0) |
+| [meituan/YOLOv6](./yolov6) | YOLOv6 系列模型 | ONNX | [Release/0.1.0](https://github.com/meituan/YOLOv6/releases/tag/0.1.0) |
 | [WongKinYiu/YOLOR](./yolor) | YOLOR 系列模型 | ONNX | [Release/weights](https://github.com/WongKinYiu/yolor/releases/tag/weights) |
 | [Megvii-BaseDetection/YOLOX](./yolox) | YOLOX 系列模型 | ONNX | [Release/v0.1.1](https://github.com/Megvii-BaseDetection/YOLOX/tree/0.1.1rc0) |
 | [WongKinYiu/ScaledYOLOv4](./scaledyolov4) | ScaledYOLOv4 系列模型 | ONNX | [CommitID: 6768003](https://github.com/WongKinYiu/ScaledYOLOv4/commit/676800364a3446900b9e8407bc880ea2127b3415) |

examples/vision/detection/nanodet_plus/README.md

@@ -8,8 +8,7 @@
 
 ## 下载预训练ONNX模型
 
-为了方便开发者的测试，下面提供了NanoDetPlus导出的各系列模型，开发者可直接下载使用。
-
+为了方便开发者的测试，下面提供了NanoDetPlus导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
 | 模型                                                               | 大小    | 精度    |
 |:---------------------------------------------------------------- |:----- |:----- |
 | [NanoDetPlus_320](https://bj.bcebos.com/paddlehub/fastdeploy/nanodet-plus-m_320.onnx ) | 4.6MB | 27.0% |

examples/vision/detection/scaledyolov4/README.md

@@ -22,8 +22,7 @@
 
 ## 下载预训练ONNX模型
 
-为了方便开发者的测试，下面提供了ScaledYOLOv4导出的各系列模型，开发者可直接下载使用。
-
+为了方便开发者的测试，下面提供了ScaledYOLOv4导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
 | 模型                                                               | 大小    | 精度    |
 |:---------------------------------------------------------------- |:----- |:----- |
 | [ScaledYOLOv4-P5](https://bj.bcebos.com/paddlehub/fastdeploy/yolov4-p5.onnx) | 271MB | 51.2% |

examples/vision/detection/yolor/README.md

@@ -2,7 +2,7 @@
 
 - YOLOR部署实现来自[YOLOR](https://github.com/WongKinYiu/yolor/releases/tag/weights)的代码，和[基于COCO的预训练模型](https://github.com/WongKinYiu/yolor/releases/tag/weights)。
 
-  - （1）[官方库](https://github.com/WongKinYiu/yolor/releases/tag/weights)提供的*.pt通过[导出ONNX模型](#导出ONNX模型)操作后，可进行部署；
+  - （1）[官方库](https://github.com/WongKinYiu/yolor/releases/tag/weights)提供的*.pt通过[导出ONNX模型](#导出ONNX模型)操作后，可进行部署，*.pose模型不支持部署；
   - （2）自己数据训练的YOLOR模型，按照[导出ONNX模型](#%E5%AF%BC%E5%87%BAONNX%E6%A8%A1%E5%9E%8B)操作后，参考[详细部署文档](#详细部署文档)完成部署。
 
 
@@ -21,8 +21,7 @@
 
 ## 下载预训练ONNX模型
 
-为了方便开发者的测试，下面提供了YOLOR导出的各系列模型，开发者可直接下载使用。
-
+为了方便开发者的测试，下面提供了YOLOR导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
 | 模型                                                               | 大小    | 精度    |
 |:---------------------------------------------------------------- |:----- |:----- |
 | [YOLOR-P6-1280](https://bj.bcebos.com/paddlehub/fastdeploy/yolor-p6-paper-541-1280-1280.onnx) | 143MB | 54.1% |

examples/vision/detection/yolov5/README.md

@@ -7,8 +7,7 @@
 
 ## 下载预训练ONNX模型
 
-为了方便开发者的测试，下面提供了YOLOv5导出的各系列模型，开发者可直接下载使用。
-
+为了方便开发者的测试，下面提供了YOLOv5导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
 | 模型                                                               | 大小    | 精度    |
 |:---------------------------------------------------------------- |:----- |:----- |
 | [YOLOv5n](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5n.onnx) | 1.9MB | 28.4% |

examples/vision/detection/yolov5lite/README.md

@@ -51,8 +51,7 @@
 
 ## 下载预训练ONNX模型
 
-为了方便开发者的测试，下面提供了YOLOv5Lite导出的各系列模型，开发者可直接下载使用。
-
+为了方便开发者的测试，下面提供了YOLOv5Lite导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
 | 模型                                                               | 大小    | 精度    |
 |:---------------------------------------------------------------- |:----- |:----- |
 | [YOLOv5Lite-e](https://bj.bcebos.com/paddlehub/fastdeploy/v5Lite-e-sim-320.onnx) | 3.1MB | 35.1% |

examples/vision/detection/yolov6/README.md

@@ -10,8 +10,7 @@
 
 ## 下载预训练ONNX模型
 
-为了方便开发者的测试，下面提供了YOLOv6导出的各系列模型，开发者可直接下载使用。
-
+为了方便开发者的测试，下面提供了YOLOv6导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
 | 模型                                                               | 大小    | 精度    |
 |:---------------------------------------------------------------- |:----- |:----- |
 | [YOLOv6s](https://bj.bcebos.com/paddlehub/fastdeploy/yolov6s.onnx) | 66MB | 43.1% |
@@ -29,4 +28,4 @@
 
 ## 版本说明
 
-- 本版本文档和代码基于[YOLOv6 0.1.0版本](https://github.com/meituan/YOLOv6/releases/download/0.1.0) 编写
+- 本版本文档和代码基于[YOLOv6 0.1.0版本](https://github.com/meituan/YOLOv6/releases/tag/0.1.0) 编写

examples/vision/detection/yolov7/README.md

@@ -25,8 +25,7 @@ python models/export.py --grid --dynamic --weights PATH/TO/yolov7.pt
 
 ## 下载预训练ONNX模型
 
-为了方便开发者的测试，下面提供了YOLOv7导出的各系列模型，开发者可直接下载使用。
-
+为了方便开发者的测试，下面提供了YOLOv7导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
 | 模型                                                               | 大小    | 精度    |
 |:---------------------------------------------------------------- |:----- |:----- |
 | [YOLOv7](https://bj.bcebos.com/paddlehub/fastdeploy/yolov7.onnx) | 141MB | 51.4% |

examples/vision/detection/yolox/README.md

@@ -10,8 +10,7 @@
 
 ## 下载预训练ONNX模型
 
-为了方便开发者的测试，下面提供了YOLOX导出的各系列模型，开发者可直接下载使用。
-
+为了方便开发者的测试，下面提供了YOLOX导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
 | 模型                                                               | 大小    | 精度    |
 |:---------------------------------------------------------------- |:----- |:----- |
 | [YOLOX-s](https://bj.bcebos.com/paddlehub/fastdeploy/yolox_s.onnx) | 35MB | 39.6% |

examples/vision/facedet/retinaface/README.md

@@ -35,8 +35,7 @@ onnxsim FaceDetector.onnx Pytorch_RetinaFace_resnet50-640-640.onnx  # resnet50
 
 ## 下载预训练ONNX模型
 
-为了方便开发者的测试，下面提供了RetinaFace导出的各系列模型，开发者可直接下载使用。
-
+为了方便开发者的测试，下面提供了RetinaFace导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
 | 模型                                                               | 大小    | 精度    |
 |:---------------------------------------------------------------- |:----- |:----- |
 | [RetinaFace_mobile0.25-640](https://bj.bcebos.com/paddlehub/fastdeploy/Pytorch_RetinaFace_mobile0.25-640-640.onnx) | 1.7MB | - |

examples/vision/facedet/scrfd/README.md

@@ -32,8 +32,7 @@
 
 ## 下载预训练ONNX模型
 
-为了方便开发者的测试，下面提供了SCRFD导出的各系列模型，开发者可直接下载使用。
-
+为了方便开发者的测试，下面提供了SCRFD导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
 | 模型                                                               | 大小    | 精度    |
 |:---------------------------------------------------------------- |:----- |:----- |
 | [SCRFD-500M-kps-160](https://bj.bcebos.com/paddlehub/fastdeploy/scrfd_500m_bnkps_shape160x160.onnx) | 2.5MB | - |

examples/vision/facedet/ultraface/README.md

@@ -9,12 +9,11 @@
 
 ## 下载预训练ONNX模型
 
-为了方便开发者的测试，下面提供了UltraFace导出的各系列模型，开发者可直接下载使用。
-
+为了方便开发者的测试，下面提供了UltraFace导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
 | 模型                                                               | 大小    | 精度    |
 |:---------------------------------------------------------------- |:----- |:----- |
-| [RFB-320](https://bj.bcebos.com/paddlehub/fastdeploy/version-RFB-320.onnx) | 1.3MB | - |
-| [RFB-320-sim](https://bj.bcebos.com/paddlehub/fastdeploy/version-RFB-320-sim.onnx) | 1.2MB | -|
+| [RFB-320](https://bj.bcebos.com/paddlehub/fastdeploy/version-RFB-320.onnx) | 1.3MB | 78.7 |
+| [RFB-320-sim](https://bj.bcebos.com/paddlehub/fastdeploy/version-RFB-320-sim.onnx) | 1.2MB | 78.7 |
 
 
 

examples/vision/facedet/yolov5face/README.md

@@ -25,8 +25,7 @@
 
 ## 下载预训练ONNX模型
 
-为了方便开发者的测试，下面提供了YOLOv5Face导出的各系列模型，开发者可直接下载使用。
-
+为了方便开发者的测试，下面提供了YOLOv5Face导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
 | 模型                                                               | 大小    | 精度    |
 |:---------------------------------------------------------------- |:----- |:----- |
 | [YOLOv5s-Face](https://bj.bcebos.com/paddlehub/fastdeploy/yolov5s-face.onnx) | 30MB | 94.3 |

examples/vision/faceid/insightface/README.md

@@ -29,8 +29,7 @@
 
 ## 下载预训练ONNX模型
 
-为了方便开发者的测试，下面提供了InsightFace导出的各系列模型，开发者可直接下载使用。
-其中精度指标来源于InsightFace中对各模型的介绍，详情各参考InsightFace中的说明
+为了方便开发者的测试，下面提供了InsightFace导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）其中精度指标来源于InsightFace中对各模型的介绍，详情各参考InsightFace中的说明
 
 | 模型                                                               | 大小    | 精度 (AgeDB_30)   |
 |:---------------------------------------------------------------- |:----- |:----- |

examples/vision/matting/modnet/README.md

@@ -18,8 +18,7 @@
 
 ## 下载预训练ONNX模型
 
-为了方便开发者的测试，下面提供了MODNet导出的各系列模型，开发者可直接下载使用。
-
+为了方便开发者的测试，下面提供了MODNet导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
 | 模型                                                               | 大小    | 精度    |
 |:---------------------------------------------------------------- |:----- |:----- |
 | [modnet_photographic](https://bj.bcebos.com/paddlehub/fastdeploy/modnet_photographic__portrait_matting.onnx) | 25MB | - |