ouceduxzk / lite.ai.toolkit

🛠 A lite C++ toolkit of awesome AI models with ONNXRuntime, NCNN, MNN and TNN. YOLOX, YOLOP, MODNet, YOLOR, NanoDet, YOLOX, SCRFD, YOLOX . MNN, NCNN, TNN, ONNXRuntime, CPU/GPU.

🛠Lite.Ai.ToolKit: A lite C++ toolkit of awesome AI models, such as Object Detection, Face Detection, Face Recognition, Segmentation, Matting, etc. See Model Zoo and ONNX Hub, MNN Hub, TNN Hub, NCNN Hub. [❤️ Star 🌟👆🏻 this repo to support me if it does any helps to you, thanks ~ ]

English | 中文文档 | MacOS | Linux | Windows

Core Features 👏👋

• Simply and User friendly. Simply and Consistent syntax like lite::cv::Type::Class, see examples.
• Minimum Dependencies. Only OpenCV and ONNXRuntime are required by default, see build.
• Lots of Algorithm Modules. Contains almost 300+ C++ re-implementations and 500+ weights.

Citations 🎉🎉

Consider to cite it as follows if you use Lite.Ai.ToolKit in your projects.

@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}
}

About Training 🤓👀

A high level Training and Evaluating Toolkit for Face Landmarks Detection is available at torchlm.

Downloads & RoadMap ✅

Some prebuilt lite.ai.toolkit libs for MacOS(x64) and Linux(x64) are available, you can download the libs from the release links. Further, prebuilt libs for Windows(x64) and Android will be coming soon ~ Please, see issues#48 for more details of the prebuilt plan and refer to releases for more available prebuilt libs.

• lite0.1.1-osx10.15.x-ocv4.5.2-ffmpeg4.2.2-onnxruntime1.8.1.zip
• lite0.1.1-osx10.15.x-ocv4.5.2-ffmpeg4.2.2-onnxruntime1.9.0.zip
• lite0.1.1-osx10.15.x-ocv4.5.2-ffmpeg4.2.2-onnxruntime1.10.0.zip
• lite0.1.1-ubuntu18.04-ocv4.5.2-ffmpeg4.2.2-onnxruntime1.8.1.zip
• lite0.1.1-ubuntu18.04-ocv4.5.2-ffmpeg4.2.2-onnxruntime1.9.0.zip
• lite0.1.1-ubuntu18.04-ocv4.5.2-ffmpeg4.2.2-onnxruntime1.10.0.zip

In Linux, in order to link the prebuilt libs, you need to export lite.ai.toolkit/lib to LD_LIBRARY_PATH first.

export LD_LIBRARY_PATH=YOUR-PATH-TO/lite.ai.toolkit/lib:$LD_LIBRARY_PATH
export LIBRARY_PATH=YOUR-PATH-TO/lite.ai.toolkit/lib:LIBRARY_PATH  # (may need)

Quick Setup 👀

To quickly setup lite.ai.toolkit, you can follow the CMakeLists.txt listed as belows. 👇👀

set(LITE_AI_DIR ${CMAKE_SOURCE_DIR}/lite.ai.toolkit)
include_directories(${LITE_AI_DIR}/include)
link_directories(${LITE_AI_DIR}/lib})
set(TOOLKIT_LIBS lite.ai.toolkit onnxruntime)
set(OpenCV_LIBS opencv_core opencv_imgcodecs opencv_imgproc opencv_video opencv_videoio)

add_executable(lite_yolov5 examples/test_lite_yolov5.cpp)
target_link_libraries(lite_yolov5 ${TOOLKIT_LIBS} ${OpenCV_LIBS})

Contents 📖💡

• Core Features
• Quick Start
• RoadMap
• Important Updates
• Supported Models Matrix
• Build Docs
• Model Zoo
• Examples
• License
• References
• Contribute

1. Quick Start 🌟🌟

Example0: Object Detection using YOLOv5. Download model from Model-Zoo².

#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;
}

2. Important Updates 🆕

Click here to see details of Important Updates!

Date	Model	C++	Paper	Code	Awesome	Type
【2022/04/03】	MODNet	link	AAAI 2022	code		matting
【2022/03/23】	PIPNtet	link	CVPR 2021	code		face::align
【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

3. Supported Models Matrix

• / = not supported now.
• ✅ = known work and official supported now.
• ✔️ = known work, but unofficial supported now.
• ❔ = in my plan, but not coming soon, maybe a few months later.

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	✅	✅	/	✅	✅	✔️	✔️	/
MODNet	24M	matting	demo	✅	✅	✅	✅	✅	✔️	✔️	/
MODNetDyn	24M	matting	demo	✅	/	/	/	✅	✔️	✔️	/
BackgroundMattingV2	20M	matting	demo	✅	✅	/	✅	✅	✔️	✔️	/
BackgroundMattingV2Dyn	20M	matting	demo	✅	/	/	/	✅	✔️	✔️	/
UltraFace	1.1M	face::detect	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
RetinaFace	1.6M	face::detect	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
FaceBoxes	3.8M	face::detect	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
FaceBoxesV2	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	✅	✅	✅	✅	✅	✔️	✔️	❔
PIPNet98	44.0M	face::align	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
PIPNet68	44.0M	face::align	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
PIPNet29	44.0M	face::align	demo	✅	✅	✅	✅	✅	✔️	✔️	❔
PIPNet19	44.0M	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	✅	✅	/	✅	✅	✔️	✔️	❔
SubPixelCNN	234K	resolution	demo	✅	✅	/	✅	✅	✔️	✔️	❔
InsectDet	27M	detection	demo	✅	✅	/	✅	✅	✔️	✔️	❔
InsectID	22M	classification	demo	✅	✅	✅	✅	✅	✅	✔️	✔️
PlantID	30M	classification	demo	✅	✅	✅	✅	✅	✅	✔️	✔️
YOLOv5BlazeFace	3.4M	face::detect	demo	✅	✅	/	/	✅	✔️	✔️	❔
YoloV5_V_6_1	7.5M	detection	demo	✅	✅	/	/	✅	✔️	✔️	❔

4. Build Docs.

• MacOS: Build the shared lib of Lite.Ai.ToolKit for MacOS from sources. Note that Lite.Ai.ToolKit uses onnxruntime as default backend, for the reason that onnxruntime supports the most of onnx's operators.

    git clone --depth=1 https://github.com/DefTruth/lite.ai.toolkit.git  # latest
    cd lite.ai.toolkit && sh ./build.sh  # On MacOS, you can use the built OpenCV, ONNXRuntime, MNN, NCNN and TNN libs in this repo.

💡 Linux and Windows.

Linux and Windows.

⚠️ Lite.Ai.ToolKit is not directly support Linux and Windows now. For Linux and Windows, you need to build or download(if have official builts) the shared libs of OpenCV、ONNXRuntime and any other Engines(like MNN, NCNN, TNN) firstly, then put the headers into the specific directories or just let these directories unchange(use the headers offer by this repo, the header file of the dependent library of this project is directly copied from the corresponding official library). However, the dynamic libraries under different operating systems need to be recompiled or downloaded. MacOS users can directly use the dynamic libraries of each dependent library provided by this project:

• 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

and put the libs into lite.ai.toolkit/lib/(linux|windows) directory. Please reference the build-docs¹ for third_party.

• lite.ai.toolkit/lib/(linux|windows)

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

Note, your also need to install ffmpeg(<=4.2.2) in Linux to support the opencv videoio module. See issue#203. In MacOS, ffmpeg4.2.2 was been package into lite.ai.toolkit, thus, no installation need in OSX. In Windows, ffmpeg was been package into opencv dll prebuilt by the team of opencv. Please make sure -DWITH_FFMPEG=ON and check the configuration info when building opencv.

• first, build ffmpeg(<=4.2.2) from source.

git clone --depth=1 https://git.ffmpeg.org/ffmpeg.git -b n4.2.2
cd ffmpeg
./configure --enable-shared --disable-x86asm --prefix=/usr/local/opt/ffmpeg --disable-static
make -j8
make install

• then, build opencv with -DWITH_FFMPEG=ON, just like

#!/bin/bash

mkdir build
cd build

cmake .. \
  -D CMAKE_BUILD_TYPE=Release \
  -D CMAKE_INSTALL_PREFIX=your-path-to-custom-dir \
  -D BUILD_TESTS=OFF \
  -D BUILD_PERF_TESTS=OFF \
  -D BUILD_opencv_python3=OFF \
  -D BUILD_opencv_python2=OFF \
  -D BUILD_SHARED_LIBS=ON \
  -D BUILD_opencv_apps=OFF \
  -D WITH_FFMPEG=ON 
  
make -j8
make install
cd ..

after built opencv, you can follow the steps to build lite.ai.toolkit.

• Windows: You can reference to issue#6

• Linux: The Docs and Docker image for Linux will be coming soon ~ issue#2

• Happy News !!! : 🚀 You can download the latest ONNXRuntime official built libs of Windows, Linux, MacOS and Arm !!! Both CPU and GPU versions are available. No more attentions needed pay to build it from source. Download the official built libs from v1.8.1. I have used version 1.7.0 for Lite.Ai.ToolKit now, you can download it from v1.7.0, but version 1.8.1 should also work, I guess ~ 🙃🤪🍀. For OpenCV, try to build from source(Linux) or down load the official built(Windows) from OpenCV 4.5.3. Then put the includes and libs into specific directory of Lite.Ai.ToolKit.

• GPU Compatibility for Windows: See issue#10.

• GPU Compatibility for Linux: See issue#97.

🔑️ How to link Lite.Ai.ToolKit?

* To link Lite.Ai.ToolKit, you can follow the CMakeLists.txt listed belows.

cmake_minimum_required(VERSION 3.10)
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

• Run the built 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

To link lite.ai.toolkit shared lib. You need to make sure that OpenCV and onnxruntime are linked correctly. A minimum example to show you how to link the shared lib of Lite.AI.ToolKit correctly for your own project can be found at CMakeLists.txt.

5. Model Zoo.

Lite.Ai.ToolKit contains 80+ AI models with 500+ frozen pretrained files now. Most of the files are converted by myself. You can use it through lite::cv::Type::Class syntax, such as lite::cv::detection::YoloV5. More details can be found at Examples for Lite.Ai.ToolKit. Note, for Google Drive, I can not upload all the *.onnx files because of the storage limitation (15G).

File	Baidu Drive	Google Drive	Docker Hub	Hub (Docs)
ONNX	Baidu Drive code: 8gin	Google Drive	ONNX Docker v0.1.22.01.08 (28G), v0.1.22.02.02 (400M)	ONNX Hub
MNN	Baidu Drive code: 9v63	❔	MNN Docker v0.1.22.01.08 (11G), v0.1.22.02.02 (213M)	MNN Hub
NCNN	Baidu Drive code: sc7f	❔	NCNN Docker v0.1.22.01.08 (9G), v0.1.22.02.02 (197M)	NCNN Hub
TNN	Baidu Drive code: 6o6k	❔	TNN Docker v0.1.22.01.08 (11G), v0.1.22.02.02 (217M)	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)
  docker pull qyjdefdocker/lite.ai.toolkit-onnx-hub:v0.1.22.02.02  # (400M) + YOLO5Face
  docker pull qyjdefdocker/lite.ai.toolkit-mnn-hub:v0.1.22.02.02   # (213M) + YOLO5Face
  docker pull qyjdefdocker/lite.ai.toolkit-ncnn-hub:v0.1.22.02.02  # (197M) + YOLO5Face
  docker pull qyjdefdocker/lite.ai.toolkit-tnn-hub:v0.1.22.02.02   # (217M) + YOLO5Face

❇️ Lite.Ai.ToolKit modules.

Namespace and Lite.Ai.ToolKit modules.

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's Classes and Pretrained Files.

Correspondence between the classes in Lite.AI.ToolKit and pretrained model files can be found at lite.ai.toolkit.hub.onnx.md. For examples, the pretrained model files for lite::cv::detection::YoloV5 and lite::cv::detection::YoloX are listed as follows.

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

It means that you can load the the any one yolov5*.onnx and yolox_*.onnx according to your application through the same Lite.AI.ToolKit's classes, such as YoloV5, YoloX, etc.

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 !

🔑️ How to download Model Zoo from Docker Hub?

• 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/

Model Hubs

The pretrained and converted ONNX files provide by lite.ai.toolkit are listed as follows. Also, see Model Zoo and ONNX Hub, MNN Hub, TNN Hub, NCNN Hub for more details.

🔑️ ONNX Model Hub

Object Detection.

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
lite::cv::detection::YoloV3	yolov3-10.onnx	onnx-models	236Mb
lite::cv::detection::TinyYoloV3	tiny-yolov3-11.onnx	onnx-models	33Mb
lite::cv::detection::YoloV4	voc-mobilenetv2-yolov4-640.onnx	YOLOv4...	176Mb
lite::cv::detection::YoloV4	voc-mobilenetv2-yolov4-416.onnx	YOLOv4...	176Mb
lite::cv::detection::SSD	ssd-10.onnx	onnx-models	76Mb
lite::cv::detection::YoloR	yolor-d6-1280-1280.onnx	yolor	667Mb
lite::cv::detection::YoloR	yolor-d6-640-640.onnx	yolor	601Mb
lite::cv::detection::YoloR	yolor-d6-320-320.onnx	yolor	584Mb
lite::cv::detection::YoloR	yolor-e6-1280-1280.onnx	yolor	530Mb
lite::cv::detection::YoloR	yolor-e6-640-640.onnx	yolor	464Mb
lite::cv::detection::YoloR	yolor-e6-320-320.onnx	yolor	448Mb
lite::cv::detection::YoloR	yolor-p6-1280-1280.onnx	yolor	214Mb
lite::cv::detection::YoloR	yolor-p6-640-640.onnx	yolor	160Mb
lite::cv::detection::YoloR	yolor-p6-320-320.onnx	yolor	147Mb
lite::cv::detection::YoloR	yolor-w6-1280-1280.onnx	yolor	382Mb
lite::cv::detection::YoloR	yolor-w6-640-640.onnx	yolor	324Mb
lite::cv::detection::YoloR	yolor-w6-320-320.onnx	yolor	309Mb
lite::cv::detection::YoloR	yolor-ssss-s2d-1280-1280.onnx	yolor	90Mb
lite::cv::detection::YoloR	yolor-ssss-s2d-640-640.onnx	yolor	49Mb
lite::cv::detection::YoloR	yolor-ssss-s2d-320-320.onnx	yolor	39Mb
lite::cv::detection::TinyYoloV4VOC	yolov4_tiny_weights_voc.onnx	yolov4-tiny...	23Mb
lite::cv::detection::TinyYoloV4VOC	yolov4_tiny_weights_voc_SE.onnx	yolov4-tiny...	23Mb
lite::cv::detection::TinyYoloV4VOC	yolov4_tiny_weights_voc_CBAM.onnx	yolov4-tiny...	23Mb
lite::cv::detection::TinyYoloV4VOC	yolov4_tiny_weights_voc_ECA.onnx	yolov4-tiny...	23Mb
lite::cv::detection::TinyYoloV4COCO	yolov4_tiny_weights_coco.onnx	yolov4-tiny...	23Mb
lite::cv::detection::ScaledYoloV4	ScaledYoloV4_yolov4-p5-1280-1280.onnx	ScaledYOLOv4	270Mb
lite::cv::detection::ScaledYoloV4	ScaledYoloV4_yolov4-p5-640-640.onnx	ScaledYOLOv4	270Mb
lite::cv::detection::ScaledYoloV4	ScaledYoloV4_yolov4-p5-320-320.onnx	ScaledYOLOv4	270Mb
lite::cv::detection::ScaledYoloV4	ScaledYoloV4_yolov4-p6-1280-1280.onnx	ScaledYOLOv4	487Mb
lite::cv::detection::ScaledYoloV4	ScaledYoloV4_yolov4-p6-640-640.onnx	ScaledYOLOv4	487Mb
lite::cv::detection::ScaledYoloV4	ScaledYoloV4_yolov4-p6-320-320.onnx	ScaledYOLOv4	487Mb
lite::cv::detection::ScaledYoloV4	ScaledYoloV4_yolov4-p7-1280-1280.onnx	ScaledYOLOv4	1.1Gb
lite::cv::detection::ScaledYoloV4	ScaledYoloV4_yolov4-p7-640-640.onnx	ScaledYOLOv4	1.1Gb
lite::cv::detection::ScaledYoloV4	ScaledYoloV4_yolov4-p5_-1280-1280.onnx	ScaledYOLOv4	270Mb
lite::cv::detection::ScaledYoloV4	ScaledYoloV4_yolov4-p5_-640-640.onnx	ScaledYOLOv4	270Mb
lite::cv::detection::ScaledYoloV4	ScaledYoloV4_yolov4-p5_-320-320.onnx	ScaledYOLOv4	270Mb
lite::cv::detection::ScaledYoloV4	ScaledYoloV4_yolov4-p6_-1280-1280.onnx	ScaledYOLOv4	487Mb
lite::cv::detection::ScaledYoloV4	ScaledYoloV4_yolov4-p6_-640-640.onnx	ScaledYOLOv4	487Mb
lite::cv::detection::ScaledYoloV4	ScaledYoloV4_yolov4-p6_-320-320.onnx	ScaledYOLOv4	487Mb
lite::cv::detection::EfficientDet	efficientdet-d0.onnx	...EfficientDet...	15Mb
lite::cv::detection::EfficientDet	efficientdet-d1.onnx	...EfficientDet...	26Mb
lite::cv::detection::EfficientDet	efficientdet-d2.onnx	...EfficientDet...	32Mb
lite::cv::detection::EfficientDet	efficientdet-d3.onnx	...EfficientDet...	49Mb
lite::cv::detection::EfficientDet	efficientdet-d4.onnx	...EfficientDet...	85Mb
lite::cv::detection::EfficientDet	efficientdet-d5.onnx	...EfficientDet...	138Mb
lite::cv::detection::EfficientDet	efficientdet-d6.onnx	...EfficientDet...	220Mb
lite::cv::detection::EfficientDetD7	efficientdet-d7.onnx	...EfficientDet...	220Mb
lite::cv::detection::EfficientDetD8	efficientdet-d8.onnx	...EfficientDet...	322Mb
lite::cv::detection::YOLOP	yolop-1280-1280.onnx	YOLOP	30Mb
lite::cv::detection::YOLOP	yolop-640-640.onnx	YOLOP	30Mb
lite::cv::detection::YOLOP	yolop-320-320.onnx	YOLOP	30Mb
lite::cv::detection::NanoDet	nanodet_m_0.5x.onnx	nanodet	1.1Mb
lite::cv::detection::NanoDet	nanodet_m.onnx	nanodet	3.6Mb
lite::cv::detection::NanoDet	nanodet_m_1.5x.onnx	nanodet	7.9Mb
lite::cv::detection::NanoDet	nanodet_m_1.5x_416.onnx	nanodet	7.9Mb
lite::cv::detection::NanoDet	nanodet_m_416.onnx	nanodet	3.6Mb
lite::cv::detection::NanoDet	nanodet_g.onnx	nanodet	14Mb
lite::cv::detection::NanoDet	nanodet_t.onnx	nanodet	5.1Mb
lite::cv::detection::NanoDet	nanodet-RepVGG-A0_416.onnx	nanodet	26Mb
lite::cv::detection::NanoDetEfficientNetLite	nanodet-EfficientNet-Lite0_320.onnx	nanodet	12Mb
lite::cv::detection::NanoDetEfficientNetLite	nanodet-EfficientNet-Lite1_416.onnx	nanodet	15Mb
lite::cv::detection::NanoDetEfficientNetLite	nanodet-EfficientNet-Lite2_512.onnx	nanodet	18Mb
lite::cv::detection::YoloX_V_0_1_1	yolox_x_v0.1.1.onnx	YOLOX	378Mb
lite::cv::detection::YoloX_V_0_1_1	yolox_l_v0.1.1.onnx	YOLOX	207Mb
lite::cv::detection::YoloX_V_0_1_1	yolox_m_v0.1.1.onnx	YOLOX	97Mb
lite::cv::detection::YoloX_V_0_1_1	yolox_s_v0.1.1.onnx	YOLOX	34Mb
lite::cv::detection::YoloX_V_0_1_1	yolox_tiny_v0.1.1.onnx	YOLOX	19Mb
lite::cv::detection::YoloX_V_0_1_1	yolox_nano_v0.1.1.onnx	YOLOX	3.5Mb
lite::cv::detection::YoloV5_V_6_0	yolov5l.640-640.v.6.0.onnx	yolov5	178Mb
lite::cv::detection::YoloV5_V_6_0	yolov5m.640-640.v.6.0.onnx	yolov5	81Mb
lite::cv::detection::YoloV5_V_6_0	yolov5s.640-640.v.6.0.onnx	yolov5	28Mb
lite::cv::detection::YoloV5_V_6_0	yolov5x.640-640.v.6.0.onnx	yolov5	331Mb
lite::cv::detection::YoloV5_V_6_0	yolov5n.640-640.v.6.0.onnx	yolov5	7.5Mb
lite::cv::detection::YoloV5_V_6_0	yolov5l6.640-640.v.6.0.onnx	yolov5	294Mb
lite::cv::detection::YoloV5_V_6_0	yolov5m6.640-640.v.6.0.onnx	yolov5	128Mb
lite::cv::detection::YoloV5_V_6_0	yolov5s6.640-640.v.6.0.onnx	yolov5	50Mb
lite::cv::detection::YoloV5_V_6_0	yolov5x6.640-640.v.6.0.onnx	yolov5	538Mb
lite::cv::detection::YoloV5_V_6_0	yolov5n6.640-640.v.6.0.onnx	yolov5	14Mb
lite::cv::detection::YoloV5_V_6_0	yolov5l6.1280-1280.v.6.0.onnx	yolov5	294Mb
lite::cv::detection::YoloV5_V_6_0	yolov5m6.1280-1280.v.6.0.onnx	yolov5	128Mb
lite::cv::detection::YoloV5_V_6_0	yolov5s6.1280-1280.v.6.0.onnx	yolov5	50Mb
lite::cv::detection::YoloV5_V_6_0	yolov5x6.1280-1280.v.6.0.onnx	yolov5	538Mb
lite::cv::detection::YoloV5_V_6_0	yolov5n6.1280-1280.v.6.0.onnx	yolov5	14Mb
lite::cv::detection::NanoDetPlus	nanodet-plus-m_320.onnx	nanodet	4.5Mb
lite::cv::detection::NanoDetPlus	nanodet-plus-m_416.onnx	nanodet	4.5Mb
lite::cv::detection::NanoDetPlus	nanodet-plus-m-1.5x_320.onnx	nanodet	9.4Mb
lite::cv::detection::NanoDetPlus	nanodet-plus-m-1.5x_416.onnx	nanodet	9.4Mb
lite::cv::detection::InsectDet	quarrying_insect_detector.onnx	InsectID	22Mb

Classification.

Class	Pretrained ONNX Files	Rename or Converted From (Repo)	Size
lite::cv::classification:EfficientNetLite4	efficientnet-lite4-11.onnx	onnx-models	49Mb
lite::cv::classification::ShuffleNetV2	shufflenet-v2-10.onnx	onnx-models	8.7Mb
lite::cv::classification::DenseNet121	densenet121.onnx	torchvision	30Mb
lite::cv::classification::GhostNet	ghostnet.onnx	torchvision	20Mb
lite::cv::classification::HdrDNet	hardnet.onnx	torchvision	13Mb
lite::cv::classification::IBNNet	ibnnet18.onnx	torchvision	97Mb
lite::cv::classification::MobileNetV2	mobilenetv2.onnx	torchvision	13Mb
lite::cv::classification::ResNet	resnet18.onnx	torchvision	44Mb
lite::cv::classification::ResNeXt	resnext.onnx	torchvision	95Mb
lite::cv::classification::InsectID	quarrying_insect_identifier.onnx	InsectID	27Mb
lite::cv::classification:PlantID	quarrying_plantid_model.onnx	PlantID	30Mb

Face Detection.

Class	Pretrained ONNX Files	Rename or Converted From (Repo)	Size
lite::cv::face::detect::UltraFace	ultraface-rfb-640.onnx	Ultra-Light...	1.5Mb
lite::cv::face::detect::UltraFace	ultraface-rfb-320.onnx	Ultra-Light...	1.2Mb
lite::cv::face::detect::RetinaFace	Pytorch_RetinaFace_resnet50.onnx	...Retinaface	104Mb
lite::cv::face::detect::RetinaFace	Pytorch_RetinaFace_resnet50-640-640.onnx	...Retinaface	104Mb
lite::cv::face::detect::RetinaFace	Pytorch_RetinaFace_resnet50-320-320.onnx	...Retinaface	104Mb
lite::cv::face::detect::RetinaFace	Pytorch_RetinaFace_resnet50-720-1080.onnx	...Retinaface	104Mb
lite::cv::face::detect::RetinaFace	Pytorch_RetinaFace_mobile0.25.onnx	...Retinaface	1.6Mb
lite::cv::face::detect::RetinaFace	Pytorch_RetinaFace_mobile[phone].onnx	...Retinaface	1.6Mb
lite::cv::face::detect::RetinaFace	Pytorch_RetinaFace_mobile[phone].onnx	...Retinaface	1.6Mb
lite::cv::face::detect::RetinaFace	Pytorch_RetinaFace_mobile[phone].onnx	...Retinaface	1.6Mb
lite::cv::face::detect::FaceBoxes	FaceBoxes.onnx	FaceBoxes	3.8Mb
lite::cv::face::detect::FaceBoxes	FaceBoxes-640-640.onnx	FaceBoxes	3.8Mb
lite::cv::face::detect::FaceBoxes	FaceBoxes-320-320.onnx	FaceBoxes	3.8Mb
lite::cv::face::detect::FaceBoxes	FaceBoxes-720-1080.onnx	FaceBoxes	3.8Mb
lite::cv::face::detect::SCRFD	scrfd_500m_shape160x160.onnx	SCRFD	2.5Mb
lite::cv::face::detect::SCRFD	scrfd_500m_shape320x320.onnx	SCRFD	2.5Mb
lite::cv::face::detect::SCRFD	scrfd_500m_shape640x640.onnx	SCRFD	2.5Mb
lite::cv::face::detect::SCRFD	scrfd_500m_bnkps_shape160x160.onnx	SCRFD	2.5Mb
lite::cv::face::detect::SCRFD	scrfd_500m_bnkps_shape320x320.onnx	SCRFD	2.5Mb
lite::cv::face::detect::SCRFD	scrfd_500m_bnkps_shape640x640.onnx	SCRFD	2.5Mb
lite::cv::face::detect::SCRFD	scrfd_1g_shape160x160.onnx	SCRFD	2.7Mb
lite::cv::face::detect::SCRFD	scrfd_1g_shape320x320.onnx	SCRFD	2.7Mb
lite::cv::face::detect::SCRFD	scrfd_1g_shape640x640.onnx	SCRFD	2.7Mb
lite::cv::face::detect::SCRFD	scrfd_2.5g_shape160x160.onnx	SCRFD	3.3Mb
lite::cv::face::detect::SCRFD	scrfd_2.5g_shape320x320.onnx	SCRFD	3.3Mb
lite::cv::face::detect::SCRFD	scrfd_2.5g_shape640x640.onnx	SCRFD	3.3Mb
lite::cv::face::detect::SCRFD	scrfd_2.5g_bnkps_shape160x160.onnx	SCRFD	3.3Mb
lite::cv::face::detect::SCRFD	scrfd_2.5g_bnkps_shape320x320.onnx	SCRFD	3.3Mb
lite::cv::face::detect::SCRFD	scrfd_2.5g_bnkps_shape640x640.onnx	SCRFD	3.3Mb
lite::cv::face::detect::SCRFD	scrfd_10g_shape640x640.onnx	SCRFD	16.9Mb
lite::cv::face::detect::SCRFD	scrfd_10g_shape1280x1280.onnx	SCRFD	16.9Mb
lite::cv::face::detect::SCRFD	scrfd_10g_bnkps_shape640x640.onnx	SCRFD	16.9Mb
lite::cv::face::detect::SCRFD	scrfd_10g_bnkps_shape1280x1280.onnx	SCRFD	16.9Mb
lite::cv::face::detect::YOLO5Face	yolov5face-blazeface-640x640.onnx	YOLO5Face	3.4Mb
lite::cv::face::detect::YOLO5Face	yolov5face-l-640x640.onnx	YOLO5Face	181Mb
lite::cv::face::detect::YOLO5Face	yolov5face-m-640x640.onnx	YOLO5Face	83Mb
lite::cv::face::detect::YOLO5Face	yolov5face-n-0.5-320x320.onnx	YOLO5Face	2.5Mb
lite::cv::face::detect::YOLO5Face	yolov5face-n-0.5-640x640.onnx	YOLO5Face	4.6Mb
lite::cv::face::detect::YOLO5Face	yolov5face-n-640x640.onnx	YOLO5Face	9.5Mb
lite::cv::face::detect::YOLO5Face	yolov5face-s-640x640.onnx	YOLO5Face	30Mb
lite::cv::face::detect::FaceBoxesV2	faceboxesv2-640x640.onnx	FaceBoxesV2	4.0Mb

Face Alignment.

Class	Pretrained ONNX Files	Rename or Converted From (Repo)	Size
lite::cv::face::align::PFLD	pfld-106-lite.onnx	pfld_106_...	1.0Mb
lite::cv::face::align::PFLD	pfld-106-v3.onnx	pfld_106_...	5.5Mb
lite::cv::face::align::PFLD	pfld-106-v2.onnx	pfld_106_...	5.0Mb
lite::cv::face::align::PFLD98	PFLD-pytorch-pfld.onnx	PFLD...	4.8Mb
lite::cv::face::align::MobileNetV268	pytorch_face_landmarks_landmark_detection_56.onnx	...landmark	9.4Mb
lite::cv::face::align::MobileNetV2SE68	pytorch_face_landmarks_landmark_detection_56_se_external.onnx	...landmark	11Mb
lite::cv::face::align::PFLD68	pytorch_face_landmarks_pfld.onnx	...landmark	2.8Mb
lite::cv::face::align::FaceLandmarks1000	FaceLandmark1000.onnx	FaceLandm...	2.0Mb
lite::cv::face::align::PIPNet98	pipnet_resnet18_10x19x32x256_aflw.onnx	PIPNet	44.0Mb
lite::cv::face::align::PIPNet68	pipnet_resnet18_10x29x32x256_cofw.onnx	PIPNet	44.0Mb
lite::cv::face::align::PIPNet29	pipnet_resnet18_10x68x32x256_300w.onnx	PIPNet	44.0Mb
lite::cv::face::align::PIPNet19	pipnet_resnet18_10x98x32x256_wflw.onnx	PIPNet	44.0Mb
lite::cv::face::align::PIPNet98	pipnet_resnet101_10x19x32x256_aflw.onnx	PIPNet	150.0Mb
lite::cv::face::align::PIPNet68	pipnet_resnet101_10x29x32x256_cofw.onnx	PIPNet	150.0Mb
lite::cv::face::align::PIPNet29	pipnet_resnet101_10x68x32x256_300w.onnx	PIPNet	150.0Mb
lite::cv::face::align::PIPNet19	pipnet_resnet101_10x98x32x256_wflw.onnx	PIPNet	150.0Mb

Face Attributes.

Class	Pretrained ONNX Files	Rename or Converted From (Repo)	Size
lite::cv::face::attr::AgeGoogleNet	age_googlenet.onnx	onnx-models	23Mb
lite::cv::face::attr::GenderGoogleNet	gender_googlenet.onnx	onnx-models	23Mb
lite::cv::face::attr::EmotionFerPlus	emotion-ferplus-7.onnx	onnx-models	33Mb
lite::cv::face::attr::EmotionFerPlus	emotion-ferplus-8.onnx	onnx-models	33Mb
lite::cv::face::attr::VGG16Age	vgg_ilsvrc_16_age_imdb_wiki.onnx	onnx-models	514Mb
lite::cv::face::attr::VGG16Age	vgg_ilsvrc_16_age_chalearn_iccv2015.onnx	onnx-models	514Mb
lite::cv::face::attr::VGG16Gender	vgg_ilsvrc_16_gender_imdb_wiki.onnx	onnx-models	512Mb
lite::cv::face::attr::SSRNet	ssrnet.onnx	SSR_Net...	190Kb
lite::cv::face::attr::EfficientEmotion7	face-emotion-recognition-enet_b0_7.onnx	face-emo...	15Mb
lite::cv::face::attr::EfficientEmotion8	face-emotion-recognition-enet_b0_8_best_afew.onnx	face-emo...	15Mb
lite::cv::face::attr::EfficientEmotion8	face-emotion-recognition-enet_b0_8_best_vgaf.onnx	face-emo...	15Mb
lite::cv::face::attr::MobileEmotion7	face-emotion-recognition-mobilenet_7.onnx	face-emo...	13Mb
lite::cv::face::attr::ReXNetEmotion7	face-emotion-recognition-affectnet_7_vggface2_rexnet150.onnx	face-emo...	30Mb

Face Recognition.

Class	Pretrained ONNX Files	Rename or Converted From (Repo)	Size
lite::cv::faceid::GlintArcFace	ms1mv3_arcface_r100.onnx	insightface	248Mb
lite::cv::faceid::GlintArcFace	ms1mv3_arcface_r50.onnx	insightface	166Mb
lite::cv::faceid::GlintArcFace	ms1mv3_arcface_r34.onnx	insightface	130Mb
lite::cv::faceid::GlintArcFace	ms1mv3_arcface_r18.onnx	insightface	91Mb
lite::cv::faceid::GlintCosFace	glint360k_cosface_r100.onnx	insightface	248Mb
lite::cv::faceid::GlintCosFace	glint360k_cosface_r50.onnx	insightface	166Mb
lite::cv::faceid::GlintCosFace	glint360k_cosface_r34.onnx	insightface	130Mb
lite::cv::faceid::GlintCosFace	glint360k_cosface_r18.onnx	insightface	91Mb
lite::cv::faceid::GlintPartialFC	partial_fc_glint360k_r100.onnx	insightface	248Mb
lite::cv::faceid::GlintPartialFC	partial_fc_glint360k_r50.onnx	insightface	91Mb
lite::cv::faceid::FaceNet	facenet_vggface2_resnet.onnx	facenet...	89Mb
lite::cv::faceid::FaceNet	facenet_casia-webface_resnet.onnx	facenet...	89Mb
lite::cv::faceid::FocalArcFace	focal-arcface-ms1m-ir152.onnx	face.evoLVe...	269Mb
lite::cv::faceid::FocalArcFace	focal-arcface-ms1m-ir50-epoch120.onnx	face.evoLVe...	166Mb
lite::cv::faceid::FocalArcFace	focal-arcface-ms1m-ir50-epoch63.onnx	face.evoLVe...	166Mb
lite::cv::faceid::FocalAsiaArcFace	focal-arcface-bh-ir50-asia.onnx	face.evoLVe...	166Mb
lite::cv::faceid::TencentCurricularFace	Tencent_CurricularFace_Backbone.onnx	TFace	249Mb
lite::cv::faceid::TencentCifpFace	Tencent_Cifp_BUPT_Balancedface_IR_34.onnx	TFace	130Mb
lite::cv::faceid::CenterLossFace	CenterLossFace_epoch_100.onnx	center-loss...	280Mb
lite::cv::faceid::SphereFace	sphere20a_20171020.onnx	sphere...	86Mb
lite::cv::faceid::PoseRobustFace	dream_cfp_res50_end2end.onnx	DREAM	92Mb
lite::cv::faceid::PoseRobustFace	dream_ijba_res18_end2end.onnx	DREAM	43Mb
lite::cv::faceid:NaivePoseRobustFace	dream_cfp_res50_naive.onnx	DREAM	91Mb
lite::cv::faceid:NaivePoseRobustFace	dream_ijba_res18_naive.onnx	DREAM	43Mb
lite::cv::faceid:MobileFaceNet	MobileFaceNet_Pytorch_068.onnx	MobileFace...	3.8Mb
lite::cv::faceid:CavaGhostArcFace	cavaface_GhostNet_x1.3_Arcface_Epoch_24.onnx	cavaface...	15Mb
lite::cv::faceid:CavaCombinedFace	cavaface_IR_SE_100_Combined_Epoch_24.onnx	cavaface...	250Mb
lite::cv::faceid:MobileSEFocalFace	face_recognition.pytorch_Mobilenet_se_focal_121000.onnx	face_recog...	4.5Mb

Head Pose Estimation.

Class	Pretrained ONNX Files	Rename or Converted From (Repo)	Size
lite::cv::face::pose::FSANet	fsanet-var.onnx	...fsanet...	1.2Mb
lite::cv::face::pose::FSANet	fsanet-1x1.onnx	...fsanet...	1.2Mb

Segmentation.

Class	Pretrained ONNX Files	Rename or Converted From (Repo)	Size
lite::cv::segmentation::DeepLabV3ResNet101	deeplabv3_resnet101_coco.onnx	torchvision	232Mb
lite::cv::segmentation::FCNResNet101	fcn_resnet101.onnx	torchvision	207Mb

Style Transfer.

Class	Pretrained ONNX Files	Rename or Converted From (Repo)	Size
lite::cv::style::FastStyleTransfer	style-mosaic-8.onnx	onnx-models	6.4Mb
lite::cv::style::FastStyleTransfer	style-candy-9.onnx	onnx-models	6.4Mb
lite::cv::style::FastStyleTransfer	style-udnie-8.onnx	onnx-models	6.4Mb
lite::cv::style::FastStyleTransfer	style-udnie-9.onnx	onnx-models	6.4Mb
lite::cv::style::FastStyleTransfer	style-pointilism-8.onnx	onnx-models	6.4Mb
lite::cv::style::FastStyleTransfer	style-pointilism-9.onnx	onnx-models	6.4Mb
lite::cv::style::FastStyleTransfer	style-rain-princess-9.onnx	onnx-models	6.4Mb
lite::cv::style::FastStyleTransfer	style-rain-princess-8.onnx	onnx-models	6.4Mb
lite::cv::style::FastStyleTransfer	style-candy-8.onnx	onnx-models	6.4Mb
lite::cv::style::FastStyleTransfer	style-mosaic-9.onnx	onnx-models	6.4Mb

Colorization.

Class	Pretrained ONNX Files	Rename or Converted From (Repo)	Size
lite::cv::colorization::Colorizer	eccv16-colorizer.onnx	colorization	123Mb
lite::cv::colorization::Colorizer	siggraph17-colorizer.onnx	colorization	129Mb

Super Resolution.

Class	Pretrained ONNX Files	Rename or Converted From (Repo)	Size
lite::cv::resolution::SubPixelCNN	subpixel-cnn.onnx	...PIXEL...	234Kb

Matting.

Class	Pretrained ONNX Files	Rename or Converted From (Repo)	Size
lite::cv::matting::RobustVideoMatting	rvm_mobilenetv3_fp32.onnx	RobustVideoMatting	14Mb
lite::cv::matting::RobustVideoMatting	rvm_mobilenetv3_fp16.onnx	RobustVideoMatting	7.2Mb
lite::cv::matting::RobustVideoMatting	rvm_resnet50_fp32.onnx	RobustVideoMatting	50Mb
lite::cv::matting::RobustVideoMatting	rvm_resnet50_fp16.onnx	RobustVideoMatting	100Mb
lite::cv::matting::MGMatting	MGMatting-DIM-100k.onnx	MGMatting	113Mb
lite::cv::matting::MGMatting	MGMatting-RWP-100k.onnx	MGMatting	113Mb
lite::cv::matting::MODNet	modnet_photographic_portrait_matting-1024x1024.onnx	MODNet	24Mb
lite::cv::matting::MODNet	modnet_photographic_portrait_matting-1024x512.onnx	MODNet	24Mb
lite::cv::matting::MODNet	modnet_photographic_portrait_matting-256x256.onnx	MODNet	24Mb
lite::cv::matting::MODNet	modnet_photographic_portrait_matting-256x512.onnx	MODNet	24Mb
lite::cv::matting::MODNet	modnet_photographic_portrait_matting-512x1024.onnx	MODNet	24Mb
lite::cv::matting::MODNet	modnet_photographic_portrait_matting-512x256.onnx	MODNet	24Mb
lite::cv::matting::MODNet	modnet_photographic_portrait_matting-512x512.onnx	MODNet	24Mb
lite::cv::matting::MODNet	modnet_webcam_portrait_matting-1024x1024.onnx	MODNet	24Mb
lite::cv::matting::MODNet	modnet_webcam_portrait_matting-1024x512.onnx	MODNet	24Mb
lite::cv::matting::MODNet	modnet_webcam_portrait_matting-256x256.onnx	MODNet	24Mb
lite::cv::matting::MODNet	modnet_webcam_portrait_matting-256x512.onnx	MODNet	24Mb
lite::cv::matting::MODNet	modnet_webcam_portrait_matting-512x1024.onnx	MODNet	24Mb
lite::cv::matting::MODNet	modnet_webcam_portrait_matting-512x256.onnx	MODNet	24Mb
lite::cv::matting::MODNet	modnet_webcam_portrait_matting-512x512.onnx	MODNet	24Mb
lite::cv::matting::MODNetDyn	modnet_photographic_portrait_matting.onnx	MODNet	24Mb
lite::cv::matting::MODNetDyn	modnet_webcam_portrait_matting.onnx	MODNet	24Mb
lite::cv::matting::BackgroundMattingV2	BGMv2_mobilenetv2-256x256-full.onnx	BackgroundMattingV2	20Mb
lite::cv::matting::BackgroundMattingV2	BGMv2_mobilenetv2-512x512-full.onnx	BackgroundMattingV2	20Mb
lite::cv::matting::BackgroundMattingV2	BGMv2_mobilenetv2-1080x1920-full.onnx	BackgroundMattingV2	20Mb
lite::cv::matting::BackgroundMattingV2	BGMv2_mobilenetv2-2160x3840-full.onnx	BackgroundMattingV2	20Mb
lite::cv::matting::BackgroundMattingV2	BGMv2_resnet50-1080x1920-full.onnx	BackgroundMattingV2	20Mb
lite::cv::matting::BackgroundMattingV2	BGMv2_resnet50-2160x3840-full.onnx	BackgroundMattingV2	20Mb
lite::cv::matting::BackgroundMattingV2	BGMv2_resnet101-2160x3840-full.onnx	BackgroundMattingV2	154Mb
lite::cv::matting::BackgroundMattingV2Dyn	BGMv2_mobilenetv2_4k_dynamic.onnx	BackgroundMattingV2	157Mb
lite::cv::matting::BackgroundMattingV2Dyn	BGMv2_mobilenetv2_hd_dynamic.onnx	BackgroundMattingV2	230Mb

6. Examples.

More examples can be found at examples.

Example0: Object Detection using YOLOv5. Download model from Model-Zoo².

#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;
}

The output is:

Or you can use Newest 🔥🔥 ! YOLO series's detector YOLOX or YoloR. They got the similar results.

More classes for general object detection (80 classes, 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!
auto *detector = new lite::cv::detection::YoloV5_V_6_0(onnx_path); 
auto *detector = new lite::cv::detection::YoloV5_V_6_1(onnx_path); 
auto *detector = new lite::cv::detection::YoloX_V_0_1_1(onnx_path);  // Newest YOLO detector !!! 2021-07

---

Example1: Video Matting using RobustVideoMatting2021🔥🔥🔥. Download model from Model-Zoo².

#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";
  std::string background_path = "../../../examples/lite/resources/test_lite_matting_bgr.jpg";
  
  auto *rvm = new lite::cv::matting::RobustVideoMatting(onnx_path, 16); // 16 threads
  std::vector<lite::types::MattingContent> contents;
  
  // 1. video matting.
  cv::Mat background = cv::imread(background_path);
  rvm->detect_video(video_path, output_path, contents, false, 0.4f,
                    20, true, true, background);
  
  delete rvm;
}

The output is:

More classes for matting (image matting, video matting, 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
auto *matting = new lite::cv::matting::MODNet(onnx_path); // AAAI 2022
auto *matting = new lite::cv::matting::MODNetDyn(onnx_path); // AAAI 2022 Dynamic Shape Inference.
auto *matting = new lite::cv::matting::BackgroundMattingV2(onnx_path); // CVPR 2020 
auto *matting = new lite::cv::matting::BackgroundMattingV2Dyn(onnx_path); // CVPR 2020 Dynamic Shape Inference.

---

Example2: 1000 Facial Landmarks Detection using FaceLandmarks1000. Download model from Model-Zoo².

#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;
}

The output is:

More classes for face alignment (68 points, 98 points, 106 points, 1000 points)

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!
auto *align = new lite::cv::face::align::PIPNet98(onnx_path);  // 98 landmarks, CVPR2021!
auto *align = new lite::cv::face::align::PIPNet68(onnx_path);  // 68 landmarks, CVPR2021!
auto *align = new lite::cv::face::align::PIPNet29(onnx_path);  // 29 landmarks, CVPR2021!
auto *align = new lite::cv::face::align::PIPNet19(onnx_path);  // 19 landmarks, CVPR2021!

---

Example3: Colorization using colorization. Download model from Model-Zoo².

#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;
}

The output is:

More classes for colorization (gray to rgb)

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

---

Example4: Face Recognition using ArcFace. Download model from Model-Zoo².

#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;
}

The output is:

Detected Sim01: 0.721159 Sim02: -0.0626267

More classes for face recognition (face id vector extract)

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 !

---

Example5: Face Detection using SCRFD 2021. Download model from Model-Zoo².

#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;
}

The output is:

More classes for face detection (super fast face detection)

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::FaceBoxesv2(onnx_path);  // 4.0Mb 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!!

Example6: Segmentation using DeepLabV3ResNet101. Download model from Model-Zoo².

#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;
}

The output is:

More classes for segmentation (human segmentation, instance segmentation)

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

Example7: Age Estimation using SSRNet . Download model from Model-Zoo².

#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;
}

The output is:

More classes for face attributes analysis (age, gender, emotion)

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!!!

Example8: 1000 Classes Classification using DenseNet. Download model from Model-Zoo².

#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;
}

The output is:

More classes for image classification (1000 classes)

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);

Example9: Head Pose Estimation using FSANet. Download model from Model-Zoo².

#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;
}

The output is:

More classes for head pose estimation (euler angle, yaw, pitch, roll)

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

Example10: Style Transfer using FastStyleTransfer. Download model from Model-Zoo².

#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;
}

The output is:

More classes for style transfer (neural style transfer, others)

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

7. License.

The code of Lite.Ai.ToolKit is released under the GPL-3.0 License.

8. References.

Many thanks to these following projects. All the Lite.AI.ToolKit's models are sourced from these repos.

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

Expand for More References.

• 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 (🔥🔥↑)

9. Compilation Options.

In addition, MNN, NCNN and TNN support for some models will be added in the future, but due to operator compatibility and some other reasons, it is impossible to ensure that all models supported by ONNXRuntime C++ can run through MNN, NCNN and TNN. So, if you want to use all the models supported by this repo and don't care about the performance gap of 1~2ms, just let ONNXRuntime as default inference engine for this repo. However, you can follow the steps below if you want to build with MNN, NCNN or TNN support.

• change the build.sh with DENABLE_MNN=ON,DENABLE_NCNN=ON or DENABLE_TNN=ON, such as

cd build && cmake \
  -DCMAKE_BUILD_TYPE=MinSizeRel \
  -DINCLUDE_OPENCV=ON \   # Whether to package OpenCV into lite.ai.toolkit, default ON; otherwise, you need to setup OpenCV yourself.
  -DENABLE_MNN=ON \       # Whether to build with MNN,  default OFF, only some models are supported now.
  -DENABLE_NCNN=OFF \     # Whether to build with NCNN, default OFF, only some models are supported now.
  -DENABLE_TNN=OFF \      # Whether to build with TNN,  default OFF, only some models are supported now.
  .. && make -j8

• use the MNN, NCNN or TNN version interface, see demo, such as

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);

10. Contribute

How to add your own models and become a contributor? See CONTRIBUTING.zh.md.

11. Many Thanks !!! 🤗🎉🎉

• Windows10 VS2019 CUDA 11.1 Build Docs (@zhanghongyong123456)
• Linux Build Docs (@lee1221ee)
• Some Windows10 Bugs Fixed (@ysc3839, @AvenSun)