🍅🍅Lite.AI.ToolKit: 一个开箱即用的C++ AI模型工具箱

English | 中文文档 | MacOS | Linux | Windows

🍅🍅Lite.AI.ToolKit: 一个轻量级的C++ AI模型工具箱，用户友好（还行吧），开箱即用。已经包括 70+ 流行的开源模型。这是一个根据个人兴趣整理的C++工具箱，, 涵盖目标检测、人脸检测、人脸识别、语义分割、抠图等领域。详见 Model Zoo 和 ONNX Hub 、MNN Hub 、TNN Hub 、NCNN Hub. [若是有用，❤️不妨给个⭐️🌟支持一下吧，感谢支持~]

核心特征及规划👏👋

用户友好，开箱即用。 使用简单一致的调用语法，如lite::cv::Type::Class，详见examples.
少量依赖，构建容易。 目前, 默认只依赖 OpenCV 和 ONNXRuntime，详见build。
众多的算法模块，且持续更新。 目前，包括 10+ 算法模块、70+ 流行的开源模型以及 500+ 权重文件

重要更新 !!

Date	Model	C++	Paper	Code	Type
【2022/01/19】	YOLO5Face	[link]	[arXiv 2021]	[code]	face::detect
【2022/01/07】	SCRFD	[link]	[CVPR 2021]	[code]	face::detect
【2021/12/27】	NanoDetPlus	[link]	[blog]	[code]	detection
【2021/12/08】	MGMatting	[link]	[CVPR 2021]	[code]	matting
【2021/11/11】	YoloV5_V_6_0	[link]	[doi]	[code]	detection
【2021/10/26】	YoloX_V_0_1_1	[link]	[arXiv 2021]	[code]	detection
【2021/10/02】	NanoDet	[link]	[blog]	[code]	detection
【2021/09/20】	RobustVideoMatting	[link]	[WACV 2022]	[code]	matting
【2021/09/02】	YOLOP	[link]	[arXiv 2021]	[code]	detection

模型支持矩阵

/ = 暂不支持.
✅ = 可以运行，且官方支持.
✔️ = 可以运行，但非官方支持.
❔ = 计划中，但不会很快实现，也许几个月后.

Class	Size	Type	Demo	ONNXRuntime	MNN	NCNN	TNN	MacOS	Linux	Windows	Android
YoloV5	28M	detection	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
YoloV3	236M	detection	demo	✅	/	/	/	✅	✔️	✔️	/
TinyYoloV3	33M	detection	demo	✅	/	/	/	✅	✔️	✔️	/
YoloV4	176M	detection	demo	✅	/	/	/	✅	✔️	✔️	/
SSD	76M	detection	demo	✅	/	/	/	✅	✔️	✔️	/
SSDMobileNetV1	27M	detection	demo	✅	/	/	/	✅	✔️	✔️	/
YoloX	3.5M	detection	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
TinyYoloV4VOC	22M	detection	demo	✅	/	/	/	✅	✔️	✔️	/
TinyYoloV4COCO	22M	detection	demo	✅	/	/	/	✅	✔️	✔️	/
YoloR	39M	detection	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
ScaledYoloV4	270M	detection	demo	✅	/	/	/	✅	✔️	✔️	/
EfficientDet	15M	detection	demo	✅	/	/	/	✅	✔️	✔️	/
EfficientDetD7	220M	detection	demo	✅	/	/	/	✅	✔️	✔️	/
EfficientDetD8	322M	detection	demo	✅	/	/	/	✅	✔️	✔️	/
YOLOP	30M	detection	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
NanoDet	1.1M	detection	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
NanoDetPlus	4.5M	detection	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
NanoDetEffi...	12M	detection	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
YoloX_V_0_1_1	3.5M	detection	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
YoloV5_V_6_0	7.5M	detection	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
GlintArcFace	92M	faceid	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
GlintCosFace	92M	faceid	demo	✅	✅	✅	✅	✅	✔️	✔️	/
GlintPartialFC	170M	faceid	demo	✅	✅	✅	✅	✅	✔️	✔️	/
FaceNet	89M	faceid	demo	✅	✅	✅	✅	✅	✔️	✔️	/
FocalArcFace	166M	faceid	demo	✅	✅	✅	✅	✅	✔️	✔️	/
FocalAsiaArcFace	166M	faceid	demo	✅	✅	✅	✅	✅	✔️	✔️	/
TencentCurricularFace	249M	faceid	demo	✅	✅	✅	✅	✅	✔️	✔️	/
TencentCifpFace	130M	faceid	demo	✅	✅	✅	✅	✅	✔️	✔️	/
CenterLossFace	280M	faceid	demo	✅	✅	✅	✅	✅	✔️	✔️	/
SphereFace	80M	faceid	demo	✅	✅	✅	✅	✅	✔️	✔️	/
PoseRobustFace	92M	faceid	demo	✅	/	/	/	✅	✔️	✔️	/
NaivePoseRobustFace	43M	faceid	demo	✅	/	/	/	✅	✔️	✔️	/
MobileFaceNet	3.8M	faceid	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
CavaGhostArcFace	15M	faceid	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
CavaCombinedFace	250M	faceid	demo	✅	✅	✅	✅	✅	✔️	✔️	/
MobileSEFocalFace	4.5M	faceid	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
RobustVideoMatting	14M	matting	demo	✅	✅	/	✅	✅	✔️	✔️	❔
MGMatting	113M	matting	demo	✅	✅	/	✅	✅	✔️	✔️	/
UltraFace	1.1M	face::detect	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
RetinaFace	1.6M	face::detect	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
FaceBoxes	3.8M	face::detect	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
SCRFD	2.5M	face::detect	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
YOLO5Face	4.8M	face::detect	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
PFLD	1.0M	face::align	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
PFLD98	4.8M	face::align	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
MobileNetV268	9.4M	face::align	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
MobileNetV2SE68	11M	face::align	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
PFLD68	2.8M	face::align	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
FaceLandmark1000	2.0M	face::align	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
FSANet	1.2M	face::pose	demo	✅	✅	/	✅	✅	✔️	✔️	❔
AgeGoogleNet	23M	face::attr	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
GenderGoogleNet	23M	face::attr	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
EmotionFerPlus	33M	face::attr	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
VGG16Age	514M	face::attr	demo	✅	✅	✅	✅	✅	✔️	✔️	/
VGG16Gender	512M	face::attr	demo	✅	✅	✅	✅	✅	✔️	✔️	/
SSRNet	190K	face::attr	demo	✅	✅	/	✅	✅	✔️	✔️	❔
EfficientEmotion7	15M	face::attr	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
EfficientEmotion8	15M	face::attr	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
MobileEmotion7	13M	face::attr	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
ReXNetEmotion7	30M	face::attr	demo	✅	✅	/	✅	✅	✔️	✔️	/
EfficientNetLite4	49M	classification	demo	✅	✅	/	✅	✅	✔️	✔️	/
ShuffleNetV2	8.7M	classification	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
DenseNet121	30.7M	classification	demo	✅	✅	✅	✅	✅	✔️	✔️	/
GhostNet	20M	classification	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
HdrDNet	13M	classification	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
IBNNet	97M	classification	demo	✅	✅	✅	✅	✅	✔️	✔️	/
MobileNetV2	13M	classification	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
ResNet	44M	classification	demo	✅	✅	✅	✅	✅	✔️	✔️	/
ResNeXt	95M	classification	demo	✅	✅	✅	✅	✅	✔️	✔️	/
DeepLabV3ResNet101	232M	segmentation	demo	✅	✅	✅	✅	✅	✔️	✔️	/
FCNResNet101	207M	segmentation	demo	✅	✅	✅	✅	✅	✔️	✔️	/
FastStyleTransfer	6.4M	style	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
Colorizer	123M	colorization	demo	✅	✅	/	✅	✅	✔️	✔️	/
SubPixelCNN	234K	resolution	demo	✅	✅	/	✅	✅	✔️	✔️	❔

1. 编译

MacOS: 从Lite.AI.ToolKit 源码编译MacOS下的动态库。需要注意的是Lite.AI.ToolKit 使用onnxruntime作为默认的后端，因为onnxruntime支持大部分onnx的原生算子，具有更高的易用性。如何编译Linux和Windows版本？点击 ▶️ 查看。

    git clone --depth=1 https://github.com/DefTruth/lite.ai.toolkit.git  # 最新源码
    cd lite.ai.toolkit && sh ./build.sh  # 对于MacOS, 你可以直接利用本项目包含的OpenCV, ONNXRuntime, MNN, NCNN and TNN依赖库，无需重新编译

💡️ Linux 和 Windows

Linux 和 Windows

⚠️ Lite.AI.ToolKit 的发行版本目前不直接支持Linux和Windows，你需要从下载Lite.AI.ToolKit的源码进行构建。首先，你需要下载(如果有官方编译好的发行版本的话)或编译OpenCV 、ONNXRuntime 和其他你需要的推理引擎，如MNN、NCNN、TNN，然后把它们的头文件分别放入各自对应的文件夹，或者直接使用本项目提供的头文件。本项目的依赖库头文件是直接从相应的官方库拷贝而来的，但不同操作系统下的动态库需要重新编译或下载，MacOS用户可以直接使用本项目提供的各个依赖库的动态库。

lite.ai.toolkit/opencv2

  cp -r you-path-to-downloaded-or-built-opencv/include/opencv4/opencv2 lite.ai.toolkit/opencv2

lite.ai.toolkit/onnxruntime

  cp -r you-path-to-downloaded-or-built-onnxruntime/include/onnxruntime lite.ai.toolkit/onnxruntime

lite.ai.toolkit/MNN

  cp -r you-path-to-downloaded-or-built-MNN/include/MNN lite.ai.toolkit/MNN

lite.ai.toolkit/ncnn

  cp -r you-path-to-downloaded-or-built-ncnn/include/ncnn lite.ai.toolkit/ncnn

lite.ai.toolkit/tnn

  cp -r you-path-to-downloaded-or-built-TNN/include/tnn lite.ai.toolkit/tnn

然后把各个依赖库拷贝到lite.ai.toolkit/lib 文件夹。请参考依赖库的编译文档¹。

lite.ai.toolkit/lib

  cp you-path-to-downloaded-or-built-opencv/lib/*opencv* lite.ai.toolkit/lib
  cp you-path-to-downloaded-or-built-onnxruntime/lib/*onnxruntime* lite.ai.toolkit/lib
  cp you-path-to-downloaded-or-built-MNN/lib/*MNN* lite.ai.toolkit/lib
  cp you-path-to-downloaded-or-built-ncnn/lib/*ncnn* lite.ai.toolkit/lib
  cp you-path-to-downloaded-or-built-TNN/lib/*TNN* lite.ai.toolkit/lib

Windows: 你可以参考issue#6 ，讨论了常见的编译问题。
Linux: 参考MacOS下的编译，替换Linux版本的依赖库即可。Linux下的发行版本将会在近期添加 ~ issue#2
令人开心的消息!!! : 🚀 你可以直接下载最新的ONNXRuntime官方构建的动态库，包含Windows, Linux, MacOS and Arm的版本!!! CPU和GPU的版本均可获得。不需要再从源码进行编译了，nice。可以从v1.8.1 下载最新的动态库. 我目前在Lite.AI.ToolKit中用的是1.7.0，你可以从v1.7.0 下载, 但1.8.1应该也是可行的。对于OpenCV，请尝试从源码构建(Linux) 或者直接从OpenCV 4.5.3 下载官方编译好的动态库(Windows). 然后把头文件和依赖库放入上述的文件夹中.
Windows GPU 兼容性: 详见issue#10.
Linux GPU 兼容性: 详见issue#97.

🔑️ 如何链接Lite.AI.ToolKit动态库?

你可参考以下的CMakeLists.txt设置来链接动态库.

cmake_minimum_required(VERSION 3.17)
project(lite.ai.toolkit.demo)

set(CMAKE_CXX_STANDARD 11)

# setting up lite.ai.toolkit
set(LITE_AI_DIR ${CMAKE_SOURCE_DIR}/lite.ai.toolkit)
set(LITE_AI_INCLUDE_DIR ${LITE_AI_DIR}/include)
set(LITE_AI_LIBRARY_DIR ${LITE_AI_DIR}/lib)
include_directories(${LITE_AI_INCLUDE_DIR})
link_directories(${LITE_AI_LIBRARY_DIR})

set(OpenCV_LIBS
        opencv_highgui
        opencv_core
        opencv_imgcodecs
        opencv_imgproc
        opencv_video
        opencv_videoio
        )
# add your executable
set(EXECUTABLE_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/examples/build)

add_executable(lite_rvm examples/test_lite_rvm.cpp)
target_link_libraries(lite_rvm
        lite.ai.toolkit
        onnxruntime
        MNN  # need, if built lite.ai.toolkit with ENABLE_MNN=ON,  default OFF
        ncnn # need, if built lite.ai.toolkit with ENABLE_NCNN=ON, default OFF 
        TNN  # need, if built lite.ai.toolkit with ENABLE_TNN=ON,  default OFF 
        ${OpenCV_LIBS})  # link lite.ai.toolkit & other libs.

cd ./build/lite.ai.toolkit/lib && otool -L liblite.ai.toolkit.0.0.1.dylib 
liblite.ai.toolkit.0.0.1.dylib:
        @rpath/liblite.ai.toolkit.0.0.1.dylib (compatibility version 0.0.1, current version 0.0.1)
        @rpath/libopencv_highgui.4.5.dylib (compatibility version 4.5.0, current version 4.5.2)
        @rpath/libonnxruntime.1.7.0.dylib (compatibility version 0.0.0, current version 1.7.0)
        ...

cd ../ && tree .
├── bin
├── include
│   ├── lite
│   │   ├── backend.h
│   │   ├── config.h
│   │   └── lite.h
│   └── ort
└── lib
    └── liblite.ai.toolkit.0.0.1.dylib

运行已经编译好的examples:

cd ./build/lite.ai.toolkit/bin && ls -lh | grep lite
-rwxr-xr-x  1 root  staff   301K Jun 26 23:10 liblite.ai.toolkit.0.0.1.dylib
...
-rwxr-xr-x  1 root  staff   196K Jun 26 23:10 lite_yolov4
-rwxr-xr-x  1 root  staff   196K Jun 26 23:10 lite_yolov5
...

./lite_yolov5
LITEORT_DEBUG LogId: ../../../hub/onnx/cv/yolov5s.onnx
=============== Input-Dims ==============
...
detected num_anchors: 25200
generate_bboxes num: 66
Default Version Detected Boxes Num: 5

为了链接lite.ai.toolkit动态库，你需要确保OpenCV and onnxruntime也被正确地链接。你可以在CMakeLists.txt 中找到一个简单且完整的，关于如何正确地链接Lite.AI.ToolKit动态库的应用案例。

2. 模型下载

Lite.AI.ToolKit 目前包括 70+ 流行的开源模型以及 500+ 文件，大部分文件是我自己转换的。你可以通过lite::cv::Type::Class 语法进行调用，如 lite::cv::detection::YoloV5。更多的细节见Examples for Lite.AI.ToolKit。注意，由于Google Driver(15G)的存储限制，我无法上传所有的模型文件，国内的小伙伴请使用百度云盘。

File	Baidu Drive	Google Drive	Docker Hub	Hub (Docs)
ONNX	Baidu Drive code: 8gin	Google Drive	ONNX Docker v0.1.22.01.08 (28G)	ONNX Hub
MNN	Baidu Drive code: 9v63	❔	MNN Docker v0.1.22.01.08 (11G)	MNN Hub
NCNN	Baidu Drive code: sc7f	❔	NCNN Docker v0.1.22.01.08 (9G)	NCNN Hub
TNN	Baidu Drive code: 6o6k	❔	TNN Docker v0.1.22.01.08 (11G)	TNN Hub

  docker pull qyjdefdocker/lite.ai.toolkit-onnx-hub:v0.1.22.01.08  # (28G)
  docker pull qyjdefdocker/lite.ai.toolkit-mnn-hub:v0.1.22.01.08   # (11G)
  docker pull qyjdefdocker/lite.ai.toolkit-ncnn-hub:v0.1.22.01.08  # (9G)
  docker pull qyjdefdocker/lite.ai.toolkit-tnn-hub:v0.1.22.01.08   # (11G)

❇️ 命名空间和Lite.AI.ToolKit算法模块的对应关系

命名空间和Lite.AI.ToolKit算法模块的对应关系

Namepace	Details
lite::cv::detection	Object Detection. one-stage and anchor-free detectors, YoloV5, YoloV4, SSD, etc. ✅
lite::cv::classification	Image Classification. DensNet, ShuffleNet, ResNet, IBNNet, GhostNet, etc. ✅
lite::cv::faceid	Face Recognition. ArcFace, CosFace, CurricularFace, etc. ❇️
lite::cv::face	Face Analysis. detect, align, pose, attr, etc. ❇️
lite::cv::face::detect	Face Detection. UltraFace, RetinaFace, FaceBoxes, PyramidBox, etc. ❇️
lite::cv::face::align	Face Alignment. PFLD(106), FaceLandmark1000(1000 landmarks), PRNet, etc. ❇️
lite::cv::face::pose	Head Pose Estimation. FSANet, etc. ❇️
lite::cv::face::attr	Face Attributes. Emotion, Age, Gender. EmotionFerPlus, VGG16Age, etc. ❇️
lite::cv::segmentation	Object Segmentation. Such as FCN, DeepLabV3, etc. ❇️ ️
lite::cv::style	Style Transfer. Contains neural style transfer now, such as FastStyleTransfer. ⚠️
lite::cv::matting	Image Matting. Object and Human matting. ❇️ ️
lite::cv::colorization	Colorization. Make Gray image become RGB. ⚠️
lite::cv::resolution	Super Resolution. ⚠️

Lite.AI.ToolKit的类与权重文件对应关系说明

Lite.AI.ToolKit的类与权重文件对应关系说明，可以在lite.ai.toolkit.hub.onnx.md 中找到。比如, lite::cv::detection::YoloV5 和 lite::cv::detection::YoloX 的权重文件为：

Class	Pretrained ONNX Files	Rename or Converted From (Repo)	Size
lite::cv::detection::YoloV5	yolov5l.onnx	yolov5 (🔥🔥💥↑)	188Mb
lite::cv::detection::YoloV5	yolov5m.onnx	yolov5 (🔥🔥💥↑)	85Mb
lite::cv::detection::YoloV5	yolov5s.onnx	yolov5 (🔥🔥💥↑)	29Mb
lite::cv::detection::YoloV5	yolov5x.onnx	yolov5 (🔥🔥💥↑)	351Mb
lite::cv::detection::YoloX	yolox_x.onnx	YOLOX (🔥🔥!!↑)	378Mb
lite::cv::detection::YoloX	yolox_l.onnx	YOLOX (🔥🔥!!↑)	207Mb
lite::cv::detection::YoloX	yolox_m.onnx	YOLOX (🔥🔥!!↑)	97Mb
lite::cv::detection::YoloX	yolox_s.onnx	YOLOX (🔥🔥!!↑)	34Mb
lite::cv::detection::YoloX	yolox_tiny.onnx	YOLOX (🔥🔥!!↑)	19Mb
lite::cv::detection::YoloX	yolox_nano.onnx	YOLOX (🔥🔥!!↑)	3.5Mb

这意味着，你可以通过Lite.AI.ToolKit中的同一个类，根据你的使用情况，加载任意一个yolov5*.onnx或yolox_*.onnx，如 YoloV5, YoloX等.

auto *yolov5 = new lite::cv::detection::YoloV5("yolov5x.onnx");  // for server
auto *yolov5 = new lite::cv::detection::YoloV5("yolov5l.onnx"); 
auto *yolov5 = new lite::cv::detection::YoloV5("yolov5m.onnx");  
auto *yolov5 = new lite::cv::detection::YoloV5("yolov5s.onnx");  // for mobile device 
auto *yolox = new lite::cv::detection::YoloX("yolox_x.onnx");  
auto *yolox = new lite::cv::detection::YoloX("yolox_l.onnx");  
auto *yolox = new lite::cv::detection::YoloX("yolox_m.onnx");  
auto *yolox = new lite::cv::detection::YoloX("yolox_s.onnx");  
auto *yolox = new lite::cv::detection::YoloX("yolox_tiny.onnx");  
auto *yolox = new lite::cv::detection::YoloX("yolox_nano.onnx");  // 3.5Mb only !

🔑️ 如何从通过Docker Hub下载Model Zoo？

Firstly, pull the image from docker hub.

docker pull qyjdefdocker/lite.ai.toolkit-mnn-hub:v0.1.22.01.08 # (11G)
docker pull qyjdefdocker/lite.ai.toolkit-ncnn-hub:v0.1.22.01.08 # (9G)
docker pull qyjdefdocker/lite.ai.toolkit-tnn-hub:v0.1.22.01.08 # (11G)
docker pull qyjdefdocker/lite.ai.toolkit-onnx-hub:v0.1.22.01.08 # (28G)

Secondly, run the container with local share dir using docker run -idt xxx. A minimum example will show you as follows.

make a share dir in your local device.

mkdir share # any name is ok.

write run_mnn_docker_hub.sh script like:

#!/bin/bash  
PORT1=6072
PORT2=6084
SERVICE_DIR=/Users/xxx/Desktop/your-path-to/share
CONRAINER_DIR=/home/hub/share
CONRAINER_NAME=mnn_docker_hub_d

docker run -idt -p ${PORT2}:${PORT1} -v ${SERVICE_DIR}:${CONRAINER_DIR} --shm-size=16gb --name ${CONRAINER_NAME} qyjdefdocker/lite.ai.toolkit-mnn-hub:v0.1.22.01.08

Finally, copy the model weights from /home/hub/mnn/cv to your local share dir.

# activate mnn docker.
sh ./run_mnn_docker_hub.sh
docker exec -it mnn_docker_hub_d /bin/bash
# copy the models to the share dir.
cd /home/hub 
cp -rf mnn/cv share/

3. 应用案例

更多的应用案例详见examples 。点击 ▶️ 可以看到该主题下更多的案例。

案例0: 使用YOLOv5 进行目标检测。请从Model-Zoo² 下载模型文件。

#include "lite/lite.h"

static void test_default()
{
  std::string onnx_path = "../../../hub/onnx/cv/yolov5s.onnx";
  std::string test_img_path = "../../../examples/lite/resources/test_lite_yolov5_1.jpg";
  std::string save_img_path = "../../../logs/test_lite_yolov5_1.jpg";

  auto *yolov5 = new lite::cv::detection::YoloV5(onnx_path); 
  std::vector<lite::types::Boxf> detected_boxes;
  cv::Mat img_bgr = cv::imread(test_img_path);
  yolov5->detect(img_bgr, detected_boxes);
  
  lite::utils::draw_boxes_inplace(img_bgr, detected_boxes);
  cv::imwrite(save_img_path, img_bgr);  
  
  delete yolov5;
}

输出的结果是:

或者你可以使用最新的 🔥🔥 ! YOLO 系列检测器YOLOX 或 YoloR ，它们会获得接近的结果。

更多可用的通用目标检测器（80类、COCO）:

auto *detector = new lite::cv::detection::YoloX(onnx_path);  // Newest YOLO detector !!! 2021-07
auto *detector = new lite::cv::detection::YoloV4(onnx_path); 
auto *detector = new lite::cv::detection::YoloV3(onnx_path); 
auto *detector = new lite::cv::detection::TinyYoloV3(onnx_path); 
auto *detector = new lite::cv::detection::SSD(onnx_path); 
auto *detector = new lite::cv::detection::YoloV5(onnx_path); 
auto *detector = new lite::cv::detection::YoloR(onnx_path);  // Newest YOLO detector !!! 2021-05
auto *detector = new lite::cv::detection::TinyYoloV4VOC(onnx_path); 
auto *detector = new lite::cv::detection::TinyYoloV4COCO(onnx_path); 
auto *detector = new lite::cv::detection::ScaledYoloV4(onnx_path); 
auto *detector = new lite::cv::detection::EfficientDet(onnx_path); 
auto *detector = new lite::cv::detection::EfficientDetD7(onnx_path); 
auto *detector = new lite::cv::detection::EfficientDetD8(onnx_path); 
auto *detector = new lite::cv::detection::YOLOP(onnx_path);
auto *detector = new lite::cv::detection::NanoDet(onnx_path); // Super fast and tiny!
auto *detector = new lite::cv::detection::NanoDetPlus(onnx_path); // Super fast and tiny! 2021/12/25
auto *detector = new lite::cv::detection::NanoDetEfficientNetLite(onnx_path); // Super fast and tiny!

案例1: 使用RobustVideoMatting2021🔥🔥🔥 进行视频抠图。请从Model-Zoo² 下载模型文件。

#include "lite/lite.h"

static void test_default()
{
  std::string onnx_path = "../../../hub/onnx/cv/rvm_mobilenetv3_fp32.onnx";
  std::string video_path = "../../../examples/lite/resources/test_lite_rvm_0.mp4";
  std::string output_path = "../../../logs/test_lite_rvm_0.mp4";
  
  auto *rvm = new lite::cv::matting::RobustVideoMatting(onnx_path, 16); // 16 threads
  std::vector<lite::types::MattingContent> contents;
  
  // 1. video matting.
  rvm->detect_video(video_path, output_path, contents, false, 0.4f);
  
  delete rvm;
}

输出的结果是:

更多可用的抠图模型（图片抠图、视频抠图、trimap/mask-free、trimap/mask-based）:

auto *matting = new lite::cv::matting::RobustVideoMatting:(onnx_path);  //  WACV 2022.
auto *matting = new lite::cv::matting::MGMatting(onnx_path); // CVPR 2021

案例2: 使用FaceLandmarks1000 进行人脸1000关键点检测。请从Model-Zoo² 下载模型文件。

#include "lite/lite.h"

static void test_default()
{
  std::string onnx_path = "../../../hub/onnx/cv/FaceLandmark1000.onnx";
  std::string test_img_path = "../../../examples/lite/resources/test_lite_face_landmarks_0.png";
  std::string save_img_path = "../../../logs/test_lite_face_landmarks_1000.jpg";
    
  auto *face_landmarks_1000 = new lite::cv::face::align::FaceLandmark1000(onnx_path);

  lite::types::Landmarks landmarks;
  cv::Mat img_bgr = cv::imread(test_img_path);
  face_landmarks_1000->detect(img_bgr, landmarks);
  lite::utils::draw_landmarks_inplace(img_bgr, landmarks);
  cv::imwrite(save_img_path, img_bgr);
  
  delete face_landmarks_1000;
}

输出的结果是:

更多可用的人脸关键点检测器（68点、98点、106点、1000点）:

auto *align = new lite::cv::face::align::PFLD(onnx_path);  // 106 landmarks, 1.0Mb only!
auto *align = new lite::cv::face::align::PFLD98(onnx_path);  // 98 landmarks, 4.8Mb only!
auto *align = new lite::cv::face::align::PFLD68(onnx_path);  // 68 landmarks, 2.8Mb only!
auto *align = new lite::cv::face::align::MobileNetV268(onnx_path);  // 68 landmarks, 9.4Mb only!
auto *align = new lite::cv::face::align::MobileNetV2SE68(onnx_path);  // 68 landmarks, 11Mb only!
auto *align = new lite::cv::face::align::FaceLandmark1000(onnx_path);  // 1000 landmarks, 2.0Mb only!

案例3: 使用colorization 进行图像着色。请从Model-Zoo² 下载模型文件。

#include "lite/lite.h"

static void test_default()
{
  std::string onnx_path = "../../../hub/onnx/cv/eccv16-colorizer.onnx";
  std::string test_img_path = "../../../examples/lite/resources/test_lite_colorizer_1.jpg";
  std::string save_img_path = "../../../logs/test_lite_eccv16_colorizer_1.jpg";
  
  auto *colorizer = new lite::cv::colorization::Colorizer(onnx_path);
  
  cv::Mat img_bgr = cv::imread(test_img_path);
  lite::types::ColorizeContent colorize_content;
  colorizer->detect(img_bgr, colorize_content);
  
  if (colorize_content.flag) cv::imwrite(save_img_path, colorize_content.mat);
  delete colorizer;
}

输出的结果是:

更多可用的着色器模型（灰度图转彩色图）:

auto *colorizer = new lite::cv::colorization::Colorizer(onnx_path);

案例4: 使用ArcFace 进行人脸识别。请从Model-Zoo² 下载模型文件。

#include "lite/lite.h"

static void test_default()
{
  std::string onnx_path = "../../../hub/onnx/cv/ms1mv3_arcface_r100.onnx";
  std::string test_img_path0 = "../../../examples/lite/resources/test_lite_faceid_0.png";
  std::string test_img_path1 = "../../../examples/lite/resources/test_lite_faceid_1.png";
  std::string test_img_path2 = "../../../examples/lite/resources/test_lite_faceid_2.png";

  auto *glint_arcface = new lite::cv::faceid::GlintArcFace(onnx_path);

  lite::types::FaceContent face_content0, face_content1, face_content2;
  cv::Mat img_bgr0 = cv::imread(test_img_path0);
  cv::Mat img_bgr1 = cv::imread(test_img_path1);
  cv::Mat img_bgr2 = cv::imread(test_img_path2);
  glint_arcface->detect(img_bgr0, face_content0);
  glint_arcface->detect(img_bgr1, face_content1);
  glint_arcface->detect(img_bgr2, face_content2);

  if (face_content0.flag && face_content1.flag && face_content2.flag)
  {
    float sim01 = lite::utils::math::cosine_similarity<float>(
        face_content0.embedding, face_content1.embedding);
    float sim02 = lite::utils::math::cosine_similarity<float>(
        face_content0.embedding, face_content2.embedding);
    std::cout << "Detected Sim01: " << sim  << " Sim02: " << sim02 << std::endl;
  }

  delete glint_arcface;
}

输出的结果是:

Detected Sim01: 0.721159 Sim02: -0.0626267

更多可用的人脸识别模型（人脸特征提取）:

auto *recognition = new lite::cv::faceid::GlintCosFace(onnx_path);  // DeepGlint(insightface)
auto *recognition = new lite::cv::faceid::GlintArcFace(onnx_path);  // DeepGlint(insightface)
auto *recognition = new lite::cv::faceid::GlintPartialFC(onnx_path); // DeepGlint(insightface)
auto *recognition = new lite::cv::faceid::FaceNet(onnx_path);
auto *recognition = new lite::cv::faceid::FocalArcFace(onnx_path);
auto *recognition = new lite::cv::faceid::FocalAsiaArcFace(onnx_path);
auto *recognition = new lite::cv::faceid::TencentCurricularFace(onnx_path); // Tencent(TFace)
auto *recognition = new lite::cv::faceid::TencentCifpFace(onnx_path); // Tencent(TFace)
auto *recognition = new lite::cv::faceid::CenterLossFace(onnx_path);
auto *recognition = new lite::cv::faceid::SphereFace(onnx_path);
auto *recognition = new lite::cv::faceid::PoseRobustFace(onnx_path);
auto *recognition = new lite::cv::faceid::NaivePoseRobustFace(onnx_path);
auto *recognition = new lite::cv::faceid::MobileFaceNet(onnx_path); // 3.8Mb only !
auto *recognition = new lite::cv::faceid::CavaGhostArcFace(onnx_path);
auto *recognition = new lite::cv::faceid::CavaCombinedFace(onnx_path);
auto *recognition = new lite::cv::faceid::MobileSEFocalFace(onnx_path); // 4.5Mb only !

案例5: 使用SCRFD 2021 进行人脸检测。请从Model-Zoo² 下载模型文件。

#include "lite/lite.h"

static void test_default()
{
  std::string onnx_path = "../../../hub/onnx/cv/scrfd_2.5g_bnkps_shape640x640.onnx";
  std::string test_img_path = "../../../examples/lite/resources/test_lite_face_detector.jpg";
  std::string save_img_path = "../../../logs/test_lite_scrfd.jpg";
  
  auto *scrfd = new lite::cv::face::detect::SCRFD(onnx_path);
  
  std::vector<lite::types::BoxfWithLandmarks> detected_boxes;
  cv::Mat img_bgr = cv::imread(test_img_path);
  scrfd->detect(img_bgr, detected_boxes);
  
  lite::utils::draw_boxes_with_landmarks_inplace(img_bgr, detected_boxes);
  cv::imwrite(save_img_path, img_bgr);
  
  std::cout << "Default Version Done! Detected Face Num: " << detected_boxes.size() << std::endl;
  
  delete scrfd;
}

输出的结果是:

更多可用的人脸检测器（轻量级人脸检测器）:

auto *detector = new lite::face::detect::UltraFace(onnx_path);  // 1.1Mb only !
auto *detector = new lite::face::detect::FaceBoxes(onnx_path);  // 3.8Mb only ! 
auto *detector = new lite::face::detect::RetinaFace(onnx_path);  // 1.6Mb only ! CVPR2020
auto *detector = new lite::face::detect::SCRFD(onnx_path);  // 2.5Mb only ! CVPR2021, Super fast and accurate!!
auto *detector = new lite::face::detect::YOLO5Face(onnx_path);  // 2021, Super fast and accurate!!

案例6: 使用 DeepLabV3ResNet101 进行语义分割. 请从Model-Zoo² 下载模型文件。

#include "lite/lite.h"

static void test_default()
{
  std::string onnx_path = "../../../hub/onnx/cv/deeplabv3_resnet101_coco.onnx";
  std::string test_img_path = "../../../examples/lite/resources/test_lite_deeplabv3_resnet101.png";
  std::string save_img_path = "../../../logs/test_lite_deeplabv3_resnet101.jpg";

  auto *deeplabv3_resnet101 = new lite::cv::segmentation::DeepLabV3ResNet101(onnx_path, 16); // 16 threads

  lite::types::SegmentContent content;
  cv::Mat img_bgr = cv::imread(test_img_path);
  deeplabv3_resnet101->detect(img_bgr, content);

  if (content.flag)
  {
    cv::Mat out_img;
    cv::addWeighted(img_bgr, 0.2, content.color_mat, 0.8, 0., out_img);
    cv::imwrite(save_img_path, out_img);
    if (!content.names_map.empty())
    {
      for (auto it = content.names_map.begin(); it != content.names_map.end(); ++it)
      {
        std::cout << it->first << " Name: " << it->second << std::endl;
      }
    }
  }
  delete deeplabv3_resnet101;
}

输出的结果是:

更多可用的语义分割模型（人像分割、实例分割）:

auto *segment = new lite::cv::segmentation::FCNResNet101(onnx_path);
auto *segment = new lite::cv::segmentation::DeepLabV3ResNet101(onnx_path);

案例7: 使用 SSRNet 进行年龄估计. 请从Model-Zoo² 下载模型文件。

#include "lite/lite.h"

static void test_default()
{
  std::string onnx_path = "../../../hub/onnx/cv/ssrnet.onnx";
  std::string test_img_path = "../../../examples/lite/resources/test_lite_ssrnet.jpg";
  std::string save_img_path = "../../../logs/test_lite_ssrnet.jpg";

  lite::cv::face::attr::SSRNet *ssrnet = new lite::cv::face::attr::SSRNet(onnx_path);

  lite::types::Age age;
  cv::Mat img_bgr = cv::imread(test_img_path);
  ssrnet->detect(img_bgr, age);
  lite::utils::draw_age_inplace(img_bgr, age);
  cv::imwrite(save_img_path, img_bgr);
  std::cout << "Default Version Done! Detected SSRNet Age: " << age.age << std::endl;

  delete ssrnet;
}

输出的结果是:

更多可用的人脸属性识别模型（性别、年龄、情绪）:

auto *attribute = new lite::cv::face::attr::AgeGoogleNet(onnx_path);  
auto *attribute = new lite::cv::face::attr::GenderGoogleNet(onnx_path); 
auto *attribute = new lite::cv::face::attr::EmotionFerPlus(onnx_path);
auto *attribute = new lite::cv::face::attr::VGG16Age(onnx_path);
auto *attribute = new lite::cv::face::attr::VGG16Gender(onnx_path);
auto *attribute = new lite::cv::face::attr::EfficientEmotion7(onnx_path); // 7 emotions, 15Mb only!
auto *attribute = new lite::cv::face::attr::EfficientEmotion8(onnx_path); // 8 emotions, 15Mb only!
auto *attribute = new lite::cv::face::attr::MobileEmotion7(onnx_path); // 7 emotions, 13Mb only!
auto *attribute = new lite::cv::face::attr::ReXNetEmotion7(onnx_path); // 7 emotions
auto *attribute = new lite::cv::face::attr::SSRNet(onnx_path); // age estimation, 190kb only!!!

案例8: 使用 DenseNet 进行图片1000分类. 请从Model-Zoo² 下载模型文件。

#include "lite/lite.h"

static void test_default()
{
  std::string onnx_path = "../../../hub/onnx/cv/densenet121.onnx";
  std::string test_img_path = "../../../examples/lite/resources/test_lite_densenet.jpg";

  auto *densenet = new lite::cv::classification::DenseNet(onnx_path);

  lite::types::ImageNetContent content;
  cv::Mat img_bgr = cv::imread(test_img_path);
  densenet->detect(img_bgr, content);
  if (content.flag)
  {
    const unsigned int top_k = content.scores.size();
    if (top_k > 0)
    {
      for (unsigned int i = 0; i < top_k; ++i)
        std::cout << i + 1
                  << ": " << content.labels.at(i)
                  << ": " << content.texts.at(i)
                  << ": " << content.scores.at(i)
                  << std::endl;
    }
  }
  delete densenet;
}

输出的结果是:

更多可用的图像分类模型（1000类）:

auto *classifier = new lite::cv::classification::EfficientNetLite4(onnx_path);  
auto *classifier = new lite::cv::classification::ShuffleNetV2(onnx_path); // 8.7Mb only!
auto *classifier = new lite::cv::classification::GhostNet(onnx_path);
auto *classifier = new lite::cv::classification::HdrDNet(onnx_path);
auto *classifier = new lite::cv::classification::IBNNet(onnx_path);
auto *classifier = new lite::cv::classification::MobileNetV2(onnx_path); // 13Mb only!
auto *classifier = new lite::cv::classification::ResNet(onnx_path); 
auto *classifier = new lite::cv::classification::ResNeXt(onnx_path);

案例9: 使用 FSANet 进行头部姿态识别. 请从Model-Zoo² 下载模型文件。

#include "lite/lite.h"

static void test_default()
{
  std::string onnx_path = "../../../hub/onnx/cv/fsanet-var.onnx";
  std::string test_img_path = "../../../examples/lite/resources/test_lite_fsanet.jpg";
  std::string save_img_path = "../../../logs/test_lite_fsanet.jpg";

  auto *fsanet = new lite::cv::face::pose::FSANet(onnx_path);
  cv::Mat img_bgr = cv::imread(test_img_path);
  lite::types::EulerAngles euler_angles;
  fsanet->detect(img_bgr, euler_angles);
  
  if (euler_angles.flag)
  {
    lite::utils::draw_axis_inplace(img_bgr, euler_angles);
    cv::imwrite(save_img_path, img_bgr);
    std::cout << "yaw:" << euler_angles.yaw << " pitch:" << euler_angles.pitch << " row:" << euler_angles.roll << std::endl;
  }
  delete fsanet;
}

输出的结果是:

更多可用的头部姿态识别模型（欧拉角、yaw、pitch、roll）:

auto *pose = new lite::cv::face::pose::FSANet(onnx_path); // 1.2Mb only!

案例10: 使用 FastStyleTransfer 进行风格迁移. 请从Model-Zoo² 下载模型文件。

#include "lite/lite.h"

static void test_default()
{
  std::string onnx_path = "../../../hub/onnx/cv/style-candy-8.onnx";
  std::string test_img_path = "../../../examples/lite/resources/test_lite_fast_style_transfer.jpg";
  std::string save_img_path = "../../../logs/test_lite_fast_style_transfer_candy.jpg";
  
  auto *fast_style_transfer = new lite::cv::style::FastStyleTransfer(onnx_path);
 
  lite::types::StyleContent style_content;
  cv::Mat img_bgr = cv::imread(test_img_path);
  fast_style_transfer->detect(img_bgr, style_content);

  if (style_content.flag) cv::imwrite(save_img_path, style_content.mat);
  delete fast_style_transfer;
}

输出的结果是:

更多可用的风格迁移模型（自然风格迁移、其他）:

auto *transfer = new lite::cv::style::FastStyleTransfer(onnx_path); // 6.4Mb only

4. 开源协议

Lite.AI.ToolKit 的代码采用GPL-3.0协议。

5. 引用参考

本项目参考了以下开源项目。

RobustVideoMatting (🔥🔥🔥new!!↑)
nanodet (🔥🔥🔥↑)
YOLOX (🔥🔥🔥new!!↑)
YOLOP (🔥🔥new!!↑)
YOLOR (🔥🔥new!!↑)
ScaledYOLOv4 (🔥🔥🔥↑)
insightface (🔥🔥🔥↑)
yolov5 (🔥🔥💥↑)
TFace (🔥🔥↑)
YOLOv4-pytorch (🔥🔥🔥↑)
Ultra-Light-Fast-Generic-Face-Detector-1MB (🔥🔥🔥↑)

展开更多引用参考

headpose-fsanet-pytorch (🔥↑)
pfld_106_face_landmarks (🔥🔥↑)
onnx-models (🔥🔥🔥↑)
SSR_Net_Pytorch (🔥↑)
colorization (🔥🔥🔥↑)
SUB_PIXEL_CNN (🔥↑)
torchvision (🔥🔥🔥↑)
facenet-pytorch (🔥↑)
face.evoLVe.PyTorch (🔥🔥🔥↑)
center-loss.pytorch (🔥🔥↑)
sphereface_pytorch (🔥🔥↑)
DREAM (🔥🔥↑)
MobileFaceNet_Pytorch (🔥🔥↑)
cavaface.pytorch (🔥🔥↑)
CurricularFace (🔥🔥↑)
face-emotion-recognition (🔥↑)
face_recognition.pytorch (🔥🔥↑)
PFLD-pytorch (🔥🔥↑)
pytorch_face_landmark (🔥🔥↑)
FaceLandmark1000 (🔥🔥↑)
Pytorch_Retinaface (🔥🔥🔥↑)
FaceBoxes (🔥🔥↑)

6. 编译选项

未来会增加一些模型的MNN 、NCNN 和 TNN 支持，但由于算子兼容等原因，也无法确保所有被ONNXRuntime C++ 支持的模型能够在MNN 、NCNN 和 TNN 下跑通。所以，如果您想使用本项目支持的所有模型，并且不在意1~2ms的性能差距的话，请使用ONNXRuntime版本的实现。ONNXRuntime 是本仓库默认的推理引擎。但是如果你确实希望编译支持MNN 、NCNN 和 TNN 支持的Lite.AI.ToolKit🍅🍅动态库，你可以按照以下的步骤进行设置。

在build.sh中添加DENABLE_MNN=ON 、DENABLE_NCNN=ON 或 DENABLE_TNN=ON，比如

cd build && cmake \
  -DCMAKE_BUILD_TYPE=MinSizeRel \
  -DINCLUDE_OPENCV=ON \   # 是否打包OpenCV进lite.ai.toolkit，默认ON；否则，你需要单独设置OpenCV
  -DENABLE_MNN=ON \       # 是否编译MNN版本的模型， 默认OFF，目前只支持部分模型
  -DENABLE_NCNN=OFF \     # 是否编译NCNN版本的模型，默认OFF，目前只支持部分模型
  -DENABLE_TNN=OFF \      # 是否编译TNN版本的模型， 默认OFF，目前只支持部分模型
  .. && make -j8

使用MNN、NCNN或TNN版本的接口，详见案例demo ，比如

auto *nanodet = new lite::mnn::cv::detection::NanoDet(mnn_path);
auto *nanodet = new lite::tnn::cv::detection::NanoDet(proto_path, model_path);
auto *nanodet = new lite::ncnn::cv::detection::NanoDet(param_path, bin_path);

7. 引用

如果您在自己的项目中使用了Lite.AI.ToolKit，可考虑按以下方式进行引用。

@misc{lite.ai.toolkit2021,
  title={lite.ai.toolkit: A lite C++ toolkit of awesome AI models.},
  url={https://github.com/DefTruth/lite.ai.toolkit},
  note={Open-source software available at https://github.com/DefTruth/lite.ai.toolkit},
  author={Yan Jun},
  year={2021}
}

8. 示例工程

Project	Describe	Operation System	Status
RobustVideoMatting.lite.ai.toolkit	Video/Image Matting	MacOS	✅
nanodet.lite.ai.toolkit	Object Detection	MacOS	✅
YOLOX.lite.ai.toolkit	Object Detection	MacOS	✅
YOLOP.lite.ai.toolkit	Panoptic Perception	MacOS	✅
scrfd.lite.ai.toolkit	Face Detection	MacOS	✅
YOLO5Face.lite.ai.toolkit	Face Detection	MacOS	✅
MGMatting.lite.ai.toolkit	Image Matting	MacOS	❔
fsanet.lite.ai.toolkit	Head Pose Estimation	MacOS	❔
ssrnet.lite.ai.toolkit	Age Estimation	MacOS	❔

若是有用，❤️不妨给个⭐️🌟支持一下吧，感谢支持~

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

README.zh.md

README.zh.md

🍅🍅Lite.AI.ToolKit: 一个开箱即用的C++ AI模型工具箱

核心特征及规划👏👋

重要更新 !!

模型支持矩阵

目录

1. 编译

Linux 和 Windows

2. 模型下载

命名空间和Lite.AI.ToolKit算法模块的对应关系

Lite.AI.ToolKit的类与权重文件对应关系说明

3. 应用案例

案例0: 使用YOLOv5 进行目标检测。请从Model-Zoo² 下载模型文件。

案例1: 使用RobustVideoMatting2021🔥🔥🔥 进行视频抠图。请从Model-Zoo² 下载模型文件。

案例2: 使用FaceLandmarks1000 进行人脸1000关键点检测。请从Model-Zoo² 下载模型文件。

案例3: 使用colorization 进行图像着色。请从Model-Zoo² 下载模型文件。

案例4: 使用ArcFace 进行人脸识别。请从Model-Zoo² 下载模型文件。

案例5: 使用SCRFD 2021 进行人脸检测。请从Model-Zoo² 下载模型文件。

案例6: 使用 DeepLabV3ResNet101 进行语义分割. 请从Model-Zoo² 下载模型文件。

案例7: 使用 SSRNet 进行年龄估计. 请从Model-Zoo² 下载模型文件。

案例8: 使用 DenseNet 进行图片1000分类. 请从Model-Zoo² 下载模型文件。

案例9: 使用 FSANet 进行头部姿态识别. 请从Model-Zoo² 下载模型文件。

案例10: 使用 FastStyleTransfer 进行风格迁移. 请从Model-Zoo² 下载模型文件。

4. 开源协议

5. 引用参考

6. 编译选项

7. 引用

8. 示例工程

Files

README.zh.md

Latest commit

History

README.zh.md

File metadata and controls

🍅🍅Lite.AI.ToolKit: 一个开箱即用的C++ AI模型工具箱

核心特征及规划👏👋

重要更新 !!

模型支持矩阵

目录

1. 编译

Linux 和 Windows

2. 模型下载

命名空间和Lite.AI.ToolKit算法模块的对应关系

Lite.AI.ToolKit的类与权重文件对应关系说明

3. 应用案例

案例0: 使用YOLOv5 进行目标检测。请从Model-Zoo2 下载模型文件。

案例1: 使用RobustVideoMatting2021🔥🔥🔥 进行视频抠图。请从Model-Zoo2 下载模型文件。

案例2: 使用FaceLandmarks1000 进行人脸1000关键点检测。请从Model-Zoo2 下载模型文件。

案例3: 使用colorization 进行图像着色。请从Model-Zoo2 下载模型文件。

案例4: 使用ArcFace 进行人脸识别。请从Model-Zoo2 下载模型文件。

案例5: 使用SCRFD 2021 进行人脸检测。请从Model-Zoo2 下载模型文件。

案例6: 使用 DeepLabV3ResNet101 进行语义分割. 请从Model-Zoo2 下载模型文件。

案例7: 使用 SSRNet 进行年龄估计. 请从Model-Zoo2 下载模型文件。

案例8: 使用 DenseNet 进行图片1000分类. 请从Model-Zoo2 下载模型文件。

案例9: 使用 FSANet 进行头部姿态识别. 请从Model-Zoo2 下载模型文件。

案例10: 使用 FastStyleTransfer 进行风格迁移. 请从Model-Zoo2 下载模型文件。

4. 开源协议

5. 引用参考

6. 编译选项

7. 引用

8. 示例工程

案例0: 使用YOLOv5 进行目标检测。请从Model-Zoo² 下载模型文件。

案例1: 使用RobustVideoMatting2021🔥🔥🔥 进行视频抠图。请从Model-Zoo² 下载模型文件。

案例2: 使用FaceLandmarks1000 进行人脸1000关键点检测。请从Model-Zoo² 下载模型文件。

案例3: 使用colorization 进行图像着色。请从Model-Zoo² 下载模型文件。

案例4: 使用ArcFace 进行人脸识别。请从Model-Zoo² 下载模型文件。

案例5: 使用SCRFD 2021 进行人脸检测。请从Model-Zoo² 下载模型文件。

案例6: 使用 DeepLabV3ResNet101 进行语义分割. 请从Model-Zoo² 下载模型文件。

案例7: 使用 SSRNet 进行年龄估计. 请从Model-Zoo² 下载模型文件。

案例8: 使用 DenseNet 进行图片1000分类. 请从Model-Zoo² 下载模型文件。

案例9: 使用 FSANet 进行头部姿态识别. 请从Model-Zoo² 下载模型文件。

案例10: 使用 FastStyleTransfer 进行风格迁移. 请从Model-Zoo² 下载模型文件。