lite.ai.toolkit Documentation

🛠Lite.Ai.ToolKit: A lite C++ toolkit of 100+ 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. Welcome to 🌟👆🏻star this repo to support me, many thanks ~ 🎉🎉

📖 News 🔥🔥

- [2026/03] Cache-DiT 🎉v1.3.0 release is ready, the major updates including: Ring Attention w/ batched P2P, USP (Hybrid Ring and Ulysses), Hybrid 2D and 3D Parallelism (💥USP + TP), VAE-P Comm overhead reduce.

- Most of my time now is focused on LLM/VLM Inference. Please check 📖Awesome-LLM-Inference and 📖LeetCUDA for more details. Now, lite.ai.toolkit is mainly maintained by 🎉@wangzijian1010.

Citations 🎉🎉

BibTeX

@misc{lite.ai.toolkit@2021,
  title={lite.ai.toolkit: A lite C++ toolkit of 100+ Awesome AI models.},
  url={https://github.com/xlite-dev/lite.ai.toolkit},
  note={Open-source software available at https://github.com/xlite-dev/lite.ai.toolkit},
  author={xlite-dev, wangzijian1010 etc},
  year={2021}
}

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.
* Many Models Supported. 300+ C++ implementations and 500+ weights 👉 Supported-Matrix.

Build 👇👇

Download prebuilt lite.ai.toolkit library from tag/v0.2.0, or just build it from source:

shell

git clone --depth=1 https://github.com/xlite-dev/lite.ai.toolkit.git  # latest
cd lite.ai.toolkit && sh ./build.sh # >= 0.2.0, support Linux only, tested on Ubuntu 20.04.6 LTS

Quick Start 🌟🌟

#### Example0: Object Detection using YOLOv5. Download model from Model-Zoo<sup>2</sup>.
``c++ #include "lite/lite.h"

int main(int argc, char *argv[]) { std::string onnx_path = "yolov5s.onnx"; std::string test_img_path = "test_yolov5.jpg"; std::string save_img_path = "test_results.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; return 0; }

text

You can download the prebuilt lite.ai.tooklit library and test resources from tag/v0.2.0.

bash
export LITE_AI_TAG_URL=https://github.com/xlite-dev/lite.ai.toolkit/releases/download/v0.2.0
wget ${LITE_AI_TAG_URL}/lite-ort1.17.1+ocv4.9.0+ffmpeg4.2.2-linux-x86_64.tgz
wget ${LITE_AI_TAG_URL}/yolov5s.onnx && wget ${LITE_AI_TAG_URL}/test_yolov5.jpg
text
#### 🎉🎉TensorRT: Boost inference performance with NVIDIA GPU via TensorRT.
Run bash ./build.sh tensorrt to build lite.ai.toolkit with TensorRT support, and then test yolov5 with the codes below. NOTE: lite.ai.toolkit need TensorRT 10.x (or later) and CUDA 12.x (or later). Please check build.sh, tensorrt-linux-x86_64-install.zh.md, test_lite_yolov5.cpp and NVIDIA/TensorRT for more details.
c++
// trtexec --onnx=yolov5s.onnx --saveEngine=yolov5s.engine
auto *yolov5 = new lite::trt::cv::detection::YOLOV5(engine_path);
text
Quick Setup 👀 
To quickly setup lite.ai.toolkit, you can follow the CMakeLists.txt listed as belows. 👇👀
cmake
set(lite.ai.toolkit_DIR YOUR-PATH-TO-LITE-INSTALL)
find_package(lite.ai.toolkit REQUIRED PATHS ${lite.ai.toolkit_DIR})
add_executable(lite_yolov5 test_lite_yolov5.cpp)
target_link_libraries(lite_yolov5 ${lite.ai.toolkit_LIBS})
text
Mixed with MNN or ONNXRuntime 👇👇 

The goal of lite.ai.toolkit is not to abstract on top of MNN and ONNXRuntime. So, you can use lite.ai.toolkit mixed with MNN(-DENABLE_MNN=ON, default OFF) or ONNXRuntime(-DENABLE_ONNXRUNTIME=ON, default ON). The lite.ai.toolkit installation package contains complete MNN and ONNXRuntime. The workflow may looks like:
C++
#include "lite/lite.h"
// 0. use yolov5 from lite.ai.toolkit to detect objs.
auto *yolov5 = new lite::cv::detection::YoloV5(onnx_path);
// 1. use OnnxRuntime or MNN to implement your own classfier.
interpreter = std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(mnn_path));
// or: session = new Ort::Session(ort_env, onnx_path, session_options);
classfier = interpreter->createSession(schedule_config);
// 2. then, classify the detected objs use your own classfier ...
text
The included headers of MNN and ONNXRuntime can be found at mnn_config.h and ort_config.h. 
<details>
<summary> 🔑️ Check the detailed Quick Start！Click here! </summary>    
Download resources 
You can download the prebuilt lite.ai.tooklit library and test resources from tag/v0.2.0.
bash
export LITE_AI_TAG_URL=https://github.com/xlite-dev/lite.ai.toolkit/releases/download/v0.2.0
wget ${LITE_AI_TAG_URL}/lite-ort1.17.1+ocv4.9.0+ffmpeg4.2.2-linux-x86_64.tgz
wget ${LITE_AI_TAG_URL}/yolov5s.onnx && wget ${LITE_AI_TAG_URL}/test_yolov5.jpg
tar -zxvf lite-ort1.17.1+ocv4.9.0+ffmpeg4.2.2-linux-x86_64.tgz
text
Write test code 
write YOLOv5 example codes and name it test_lite_yolov5.cpp:
c++
#include "lite/lite.h"
int main(int argc, char *argv[]) {
  std::string onnx_path = "yolov5s.onnx";
  std::string test_img_path = "test_yolov5.jpg";
  std::string save_img_path = "test_results.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;
  return 0;
}
text
Setup CMakeLists.txt  
cmake
cmake_minimum_required(VERSION 3.10)
project(lite_yolov5)
set(CMAKE_CXX_STANDARD 17)
set(lite.ai.toolkit_DIR YOUR-PATH-TO-LITE-INSTALL)
find_package(lite.ai.toolkit REQUIRED PATHS ${lite.ai.toolkit_DIR})
if (lite.ai.toolkit_Found)
    message(STATUS "lite.ai.toolkit_INCLUDE_DIRS: ${lite.ai.toolkit_INCLUDE_DIRS}")
    message(STATUS "        lite.ai.toolkit_LIBS: ${lite.ai.toolkit_LIBS}")
    message(STATUS "   lite.ai.toolkit_LIBS_DIRS: ${lite.ai.toolkit_LIBS_DIRS}")
endif()
add_executable(lite_yolov5 test_lite_yolov5.cpp)
target_link_libraries(lite_yolov5 ${lite.ai.toolkit_LIBS})
text
Build example 
bash
mkdir build && cd build && cmake .. && make -j1
text
Then, export the lib paths to LD_LIBRARY_PATH which listed by lite.ai.toolkit_LIBS_DIRS.
bash
export LD_LIBRARY_PATH=YOUR-PATH-TO-LITE-INSTALL/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=YOUR-PATH-TO-LITE-INSTALL/third_party/opencv/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=YOUR-PATH-TO-LITE-INSTALL/third_party/onnxruntime/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=YOUR-PATH-TO-LITE-INSTALL/third_party/MNN/lib:$LD_LIBRARY_PATH # if -DENABLE_MNN=ON
text
Run binary: 
bash
cp ../yolov5s.onnx ../test_yolov.jpg .
./lite_yolov5
text
The output logs:
bash
LITEORT_DEBUG LogId: ../examples/hub/onnx/cv/yolov5s.onnx
=============== Input-Dims ==============
Name: images
Dims: 1
Dims: 3
Dims: 640
Dims: 640
=============== Output-Dims ==============
Output: 0 Name: pred Dim: 0 :1
Output: 0 Name: pred Dim: 1 :25200
Output: 0 Name: pred Dim: 2 :85
Output: 1 Name: output2 Dim: 0 :1
......
Output: 3 Name: output4 Dim: 1 :3
Output: 3 Name: output4 Dim: 2 :20
Output: 3 Name: output4 Dim: 3 :20
Output: 3 Name: output4 Dim: 4 :85
========================================
detected num_anchors: 25200
generate_bboxes num: 48
text
</details>
  
<div id="lite.ai.toolkit-Supported-Models-Matrix"></div>
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.
NVIDIA GPU Inference: TensorRT 
|Class|Class|Class|Class|Class| System | Engine |  
|:---:|:---:|:---:|:---:|:---:|:---:|:---:|      
|✅YOLOv5|✅YOLOv6|✅YOLOv8|✅YOLOv8Face|✅YOLOv5Face|  Linux | TensorRT |  
|✅YOLOX|✅YOLOv5BlazeFace |✅StableDiffusion| ✅FaceFusion |  / |  Linux | TensorRT |

CPU Inference: ONNXRuntime, MNN, NCNN and TNN 

|                                                       Class                                                       | Size  |       Type       |                                                          Demo                                                          | ONNXRuntime | MNN | NCNN | TNN | Linux | MacOS | 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        |      ✅      |  ✅  |  /   |  /  |   ✅   |  ✔️   |   ✔️    |    ❔    |
|                             HeadSeg                             |  31M  |  segmentation  |         demo          |      ✅      |  ✅  |  /   |  ✅  |   ✅   |  ✔️   |   ✔️    |    ❔    |
|                       FemalePhoto2Cartoon                       |  15M  |     style      |   demo    |      ✅      |  ✅  |  /   |  ✅  |   ✅   |  ✔️   |   ✔️    |    ❔    |
|                    FastPortraitSeg                     | 400k  |  segmentation  |     demo     |      ✅      |  ✅  |  /   |  /  |   ✅   |  ✔️   |   ✔️    |    ❔    |
|                     PortraitSegSINet                      | 380k  |  segmentation  |    demo     |      ✅      |  ✅  |  /   |  /  |   ✅   |  ✔️   |   ✔️    |    ❔    |
|                  PortraitSegExtremeC3Net                  | 180k  |  segmentation  | demo |      ✅      |  ✅  |  /   |  /  |   ✅   |  ✔️   |   ✔️    |    ❔    |
|                                 FaceHairSeg                                 |  18M  |  segmentation  |       demo       |      ✅      |  ✅  |  /   |  /  |   ✅   |  ✔️   |   ✔️    |    ❔    |
|                       HairSeg                        |  18M  |  segmentation  |         demo          |      ✅      |  ✅  |  /   |  /  |   ✅   |  ✔️   |   ✔️    |    ❔    |
|                MobileHumanMatting                 |  3M   |    matting     |   demo    |      ✅      |  ✅  |  /   |  /  |   ✅   |  ✔️   |   ✔️    |    ❔    |
|                 MobileHairSeg                  |  14M  |  segmentation  |      demo      |      ✅      |  ✅  |  /   |  /  |   ✅   |  ✔️   |   ✔️    |    ❔    |
|                                    YOLOv6                                    |  17M  |   detection    |          demo           |      ✅      |  ✅  |  ✅   |  ✅  |   ✅   |  ✔️   |   ✔️    |    ❔    |
|                     FaceParsingBiSeNet                      |  50M  |  segmentation  |   demo    |      ✅      |  ✅  |  ✅   |  ✅  |   ✅   |  ✔️   |   ✔️    |    ❔    |
|                    FaceParsingBiSeNetDyn                    |  50M  |  segmentation  | demo  |      ✅      |  /  |  /   |  /  |   /   |  ✔️   |   ✔️    |    ❔    |
<div id="lite.ai.toolkit-Model-Zoo"></div>
<details>
<summary> 🔑️ Model Zoo！Click here! </summary>    
Model Zoo. 
<div id="lite.ai.toolkit-2"></div>
Lite.Ai.ToolKit contains almost 100+ 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  |
shell
  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
text
🔑️ How to download Model Zoo from Docker Hub? 
* Firstly, pull the image from docker hub.
shell
  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)
  text
* 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.
shell
  mkdir share # any name is ok.
  text
* write run_mnn_docker_hub.sh script like:
shell
  #!/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
  
text
* Finally, copy the model weights from /home/hub/mnn/cv to your local share dir.
shell
  # 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/
  text
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.
</details>

<div id="lite.ai.toolkit-Examples-for-Lite.AI.ToolKit"></div>
<details>
<summary> 🔑️ More Examples！Click here! </summary>    
🔑️ More Examples. 
More examples can be found at examples. 
<div id="lite.ai.toolkit-object-detection"></div>#### Example0: Object Detection using YOLOv5. Download model from Model-Zoo<sup>2</sup>.
c++
#include "lite/lite.h"
static void test_default()
{
  std::string onnx_path = "../../../examples/hub/onnx/cv/yolov5s.onnx";
  std::string test_img_path = "../../../examples/lite/resources/test_lite_yolov5_1.jpg";
  std::string save_img_path = "../../../examples/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;
}
text
The output is:
<div align='center'>
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/44dbf4ac-0f38-41b6-930b-55b032b3c2ee' height="256px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/23aca3df-76a6-45c0-a48b-7968b4d4b9d8' height="256px">
</div>
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).

/ Detailed source-code truncated for AI context efficiency. /
text
<div id="lite.ai.toolkit-matting"></div>  #### Example1: Video Matting using RobustVideoMatting2021🔥🔥🔥. Download model from Model-Zoo<sup>2</sup>.
c++
#include "lite/lite.h"
static void test_default()
{
  std::string onnx_path = "../../../examples/hub/onnx/cv/rvm_mobilenetv3_fp32.onnx";
  std::string video_path = "../../../examples/lite/resources/test_lite_rvm_0.mp4";
  std::string output_path = "../../../examples/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;
}
text
The output is:
<div align='center'>
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/a6da4814-6643-4dfc-89ce-57f140c999fc' height="150px" width="150px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/9e4f060e-3de8-44c4-a20f-74a0ff3943bb' height="150px" width="150px">  
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/42bb2991-333a-4524-b874-6ab6156b3425' height="150px" width="150px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/f8d65d8c-2a3d-4634-9169-3bc36452d997' height="150px" width="150px">
  <br>
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/c1411bb7-5537-4d6e-81f7-c902c2256a72' height="150px" width="150px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/6344f307-15e3-4593-9866-50f5ee777f43' height="150px" width="150px">  
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/4d824828-7727-48df-8aae-64e15ca1c03b' height="150px" width="150px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/e8102fd6-e869-4a42-a19f-dd6d180dda92' height="150px" width="150px">
</div>More classes for matting (image matting, video matting, trimap/mask-free, trimap/mask-based)
c++
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.
auto *matting = new lite::cv::matting::MobileHumanMatting(onnx_path); // 3Mb only !!!
text
<div id="lite.ai.toolkit-face-alignment"></div>#### Example2: 1000 Facial Landmarks Detection using FaceLandmarks1000. Download model from Model-Zoo<sup>2</sup>.
c++
#include "lite/lite.h"
static void test_default()
{
  std::string onnx_path = "../../../examples/hub/onnx/cv/FaceLandmark1000.onnx";
  std::string test_img_path = "../../../examples/lite/resources/test_lite_face_landmarks_0.png";
  std::string save_img_path = "../../../examples/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;
}
text
The output is:
<div align='center'>
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/318691ec-7226-4d55-990b-a320635d8910' height="224px" width="224px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/d64ae30e-a0b9-4ac9-bf4f-9d6f80c2c05a' height="224px" width="224px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/c802858c-6899-4246-8839-5721c43faffe' height="224px" width="224px">
</div>    More classes for face alignment (68 points, 98 points, 106 points, 1000 points)
c++
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!
text
<div id="lite.ai.toolkit-colorization"></div>#### Example3: Colorization using colorization. Download model from Model-Zoo<sup>2</sup>.
c++
#include "lite/lite.h"
static void test_default()
{
  std::string onnx_path = "../../../examples/hub/onnx/cv/eccv16-colorizer.onnx";
  std::string test_img_path = "../../../examples/lite/resources/test_lite_colorizer_1.jpg";
  std::string save_img_path = "../../../examples/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;
}
text
The output is:
<div align='center'>
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/022dd4ab-1048-4d51-8e84-f839464d013e' height="224px" width="224px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/8eceb121-6da6-41d9-8dbf-949034f27247' height="224px" width="224px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/54a9b811-d21b-4120-8381-df0f858dba8b' height="224px" width="224px">  
  <br> 
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/95f265a8-ca85-4df1-b2f0-04e1dd3d8fff' height="224px" width="224px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/34bd3bcb-377a-47a0-b8bf-a44603f9b275' height="224px" width="224px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/e1eff9e9-9f3c-4558-8826-d05c0c254e29' height="224px" width="224px">
</div>    More classes for colorization (gray to rgb)
c++
auto *colorizer = new lite::cv::colorization::Colorizer(onnx_path);
text
<div id="lite.ai.toolkit-face-recognition"></div>  #### Example4: Face Recognition using ArcFace. Download model from Model-Zoo<sup>2</sup>.
c++
#include "lite/lite.h"
static void test_default()
{
  std::string onnx_path = "../../../examples/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;
}
text
The output is:
<div align='center'>
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/8311a1e0-1945-4a70-a361-c15a6e55baab' height="224px" width="224px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/c1619f3f-cb12-4607-9e72-4a9f9224ef09' height="224px" width="224px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/21859087-8458-4be6-b1ab-f20c1546e310' height="224px" width="224px">
</div>  
Detected Sim01: 0.721159  Sim02: -0.0626267
More classes for face recognition (face id vector extract)
c++
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 !
text
<div id="lite.ai.toolkit-face-detection"></div>#### Example5: Face Detection using SCRFD 2021. Download model from Model-Zoo<sup>2</sup>.
c++
#include "lite/lite.h"
static void test_default()
{
  std::string onnx_path = "../../../examples/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 = "../../../examples/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);
  
  delete scrfd;
}
text
The output is:
<div align='center'>
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/b913c502-93fc-4a29-8114-9a3450c512f0' height="224px" width="224px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/28274741-8745-4665-abff-3a384b75f7fa' height="224px" width="224px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/a0bc4d9f-df88-4757-bcfb-214f2c1d4991' height="224px" width="224px">
</div>  More classes for face detection (super fast face detection)
c++
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!!
auto *detector = new lite::face::detect::YOLOv5BlazeFace(onnx_path);  // 2021, Super fast and accurate!!
text
<div id="lite.ai.toolkit-segmentation"></div>   #### Example6: Object Segmentation using DeepLabV3ResNet101. Download model from Model-Zoo<sup>2</sup>.
c++
#include "lite/lite.h"
static void test_default()
{
  std::string onnx_path = "../../../examples/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 = "../../../examples/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;
}
text
The output is:
<div align='center'>
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/cf143f25-a233-40f1-a4b9-7ad52f691799' height="256px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/f4dd5263-8514-4bb0-a0dd-dbe532481aff' height="256px">
</div> More classes for object segmentation (general objects segmentation)
c++
auto *segment = new lite::cv::segmentation::FCNResNet101(onnx_path);
auto *segment = new lite::cv::segmentation::DeepLabV3ResNet101(onnx_path);
text
<div id="lite.ai.toolkit-face-attributes-analysis"></div>  #### Example7: Age Estimation using SSRNet . Download model from Model-Zoo<sup>2</sup>.
c++
#include "lite/lite.h"
static void test_default()
{
  std::string onnx_path = "../../../examples/hub/onnx/cv/ssrnet.onnx";
  std::string test_img_path = "../../../examples/lite/resources/test_lite_ssrnet.jpg";
  std::string save_img_path = "../../../examples/logs/test_lite_ssrnet.jpg";
  auto *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);
  delete ssrnet;
}
text
The output is:
<div align='center'>
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/6dc688d9-95be-40f3-b9b8-1a2f69e39e1d' height="224px" width="224px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/77089174-f744-4603-b417-c23caeb344d7' height="224px" width="224px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/8f279483-5849-4356-885c-5806337ced2a' height="224px" width="224px">
</div>    More classes for face attributes analysis (age, gender, emotion)
c++
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!!!
text
<div id="lite.ai.toolkit-image-classification"></div>  #### Example8: 1000 Classes Classification using DenseNet. Download model from Model-Zoo<sup>2</sup>.
c++
#include "lite/lite.h"
static void test_default()
{
  std::string onnx_path = "../../../examples/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;
}
text
The output is:
<div align='center'>
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/58e3b905-367d-486a-a3b6-062cef87d726' height="224px" width="350px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/cf81d298-5903-4d3c-ad06-14882911c006' height="224px" width="350px">
</div>  More classes for image classification (1000 classes)
c++
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);
text
<div id="lite.ai.toolkit-head-pose-estimation"></div>  #### Example9: Head Pose Estimation using FSANet. Download model from Model-Zoo<sup>2</sup>.
c++
#include "lite/lite.h"
static void test_default()
{
  std::string onnx_path = "../../../examples/hub/onnx/cv/fsanet-var.onnx";
  std::string test_img_path = "../../../examples/lite/resources/test_lite_fsanet.jpg";
  std::string save_img_path = "../../../examples/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;
}
text
The output is:
<div align='center'>
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/20a18d56-297c-4c72-8153-76d4380fc9ec)' height="224px" width="224px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/6630a13b-af81-4606-8a81-37fb416f0a64' height="224px" width="224px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/fb873266-2cfd-4b08-8ffb-639aee1ca2b6' height="224px" width="224px">
</div>  More classes for head pose estimation (euler angle, yaw, pitch, roll)
c++
auto *pose = new lite::cv::face::pose::FSANet(onnx_path); // 1.2Mb only!
text
<div id="lite.ai.toolkit-style-transfer"></div>#### Example10: Style Transfer using FastStyleTransfer. Download model from Model-Zoo<sup>2</sup>.
c++
#include "lite/lite.h"
static void test_default()
{
  std::string onnx_path = "../../../examples/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 = "../../../examples/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;
}
text
The output is:
<div align='center'>
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/c42af6ea-0b3a-4816-902a-9958fdef5653' height="224px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/bbaa6e7e-50c0-4993-b6e9-aee681e61fdb' height="224px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/95106e7e-c6bc-433d-b20c-95b579e85a06' height="224px">  
  <br> 
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/13a17444-27a4-4153-a6ee-5fff0a7fc667' height="224px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/6e2c1d8b-f4a2-4433-b31b-b60f381344c1' height="224px">
  <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/9f3f706a-50b7-43e4-8631-13ffa9b12fb5' height="224px">
</div>More classes for style transfer (neural style transfer, others)
c++
auto *transfer = new lite::cv::style::FastStyleTransfer(onnx_path); // 6.4Mb only
text
 #### Example11: Human Head Segmentation using HeadSeg. Download model from Model-Zoo<sup>2</sup>.
c++
#include "lite/lite.h"
static void test_default()
{
  std::string onnx_path = "../../../examples/hub/onnx/cv/minivision_head_seg.onnx";
  std::string test_img_path = "../../../examples/lite/resources/test_lite_head_seg.png";
  std::string save_img_path = "../../../examples/logs/test_lite_head_seg.jpg";
  auto *head_seg = new lite::cv::segmentation::HeadSeg(onnx_path, 4); // 4 threads
  lite::types::HeadSegContent content;
  cv::Mat img_bgr = cv::imread(test_img_path);
  head_seg->detect(img_bgr, content);
  if (content.flag) cv::imwrite(save_img_path, content.mask * 255.f);

delete head_seg; }`The output is:

More classes for human segmentation (head, portrait, hair, others)`c++ auto *segment = new lite::cv::segmentation::HeadSeg(onnx_path); // 31Mb auto *segment = new lite::cv::segmentation::FastPortraitSeg(onnx_path); // <= 400Kb !!! auto *segment = new lite::cv::segmentation::PortraitSegSINet(onnx_path); // <= 380Kb !!! auto *segment = new lite::cv::segmentation::PortraitSegExtremeC3Net(onnx_path); // <= 180Kb !!! Extreme Tiny !!! auto *segment = new lite::cv::segmentation::FaceHairSeg(onnx_path); // 18M auto *segment = new lite::cv::segmentation::HairSeg(onnx_path); // 18M auto *segment = new lite::cv::segmentation::MobileHairSeg(onnx_path); // 14M

text

 #### Example12: Photo transfer to Cartoon Photo2Cartoon. Download model from Model-Zoo<sup>2</sup>.

c++
#include "lite/lite.h"
static void test_default()
{
  std::string head_seg_onnx_path = "../../../examples/hub/onnx/cv/minivision_head_seg.onnx";
  std::string cartoon_onnx_path = "../../../examples/hub/onnx/cv/minivision_female_photo2cartoon.onnx";
  std::string test_img_path = "../../../examples/lite/resources/test_lite_female_photo2cartoon.jpg";
  std::string save_mask_path = "../../../examples/logs/test_lite_female_photo2cartoon_seg.jpg";
  std::string save_cartoon_path = "../../../examples/logs/test_lite_female_photo2cartoon_cartoon.jpg";
  auto *head_seg = new lite::cv::segmentation::HeadSeg(head_seg_onnx_path, 4); // 4 threads
  auto *female_photo2cartoon = new lite::cv::style::FemalePhoto2Cartoon(cartoon_onnx_path, 4); // 4 threads
  lite::types::HeadSegContent head_seg_content;
  cv::Mat img_bgr = cv::imread(test_img_path);
  head_seg->detect(img_bgr, head_seg_content);
  if (head_seg_content.flag && !head_seg_content.mask.empty())
  {
    cv::imwrite(save_mask_path, head_seg_content.mask * 255.f);
    // Female Photo2Cartoon Style Transfer
    lite::types::FemalePhoto2CartoonContent female_cartoon_content;
    female_photo2cartoon->detect(img_bgr, head_seg_content.mask, female_cartoon_content);
    
    if (female_cartoon_content.flag && !female_cartoon_content.cartoon.empty())
      cv::imwrite(save_cartoon_path, female_cartoon_content.cartoon);
  }

delete head_seg; delete female_photo2cartoon; }`The output is:

More classes for photo style transfer.`c++ auto *transfer = new lite::cv::style::FemalePhoto2Cartoon(onnx_path);

text

 #### Example13: Face Parsing using FaceParsing. Download model from Model-Zoo<sup>2</sup>.

c++
#include "lite/lite.h"
static void test_default()
{
  std::string onnx_path = "../../../examples/hub/onnx/cv/face_parsing_512x512.onnx";
  std::string test_img_path = "../../../examples/lite/resources/test_lite_face_parsing.png";
  std::string save_img_path = "../../../examples/logs/test_lite_face_parsing_bisenet.jpg";
  auto *face_parsing_bisenet = new lite::cv::segmentation::FaceParsingBiSeNet(onnx_path, 8); // 8 threads
  lite::types::FaceParsingContent content;
  cv::Mat img_bgr = cv::imread(test_img_path);
  face_parsing_bisenet->detect(img_bgr, content);

if (content.flag && !content.merge.empty()) cv::imwrite(save_img_path, content.merge); delete face_parsing_bisenet; }`The output is:

More classes for face parsing (hair, eyes, nose, mouth, others)`c++ auto *segment = new lite::cv::segmentation::FaceParsingBiSeNet(onnx_path); // 50Mb auto *segment = new lite::cv::segmentation::FaceParsingBiSeNetDyn(onnx_path); // Dynamic Shape Inference.``
</details>

©️License

GNU General Public License v3.0

🎉Contribute

Please consider ⭐ this repo if you like it, as it is the simplest way to support me.

📖 News 🔥🔥 Copy Chapter

Citations 🎉🎉 Copy Chapter

Features 👏👋 Copy Chapter

Build 👇👇 Copy Chapter

Quick Start 🌟🌟 Copy Chapter

Quick Setup 👀 Copy Chapter

Mixed with MNN or ONNXRuntime 👇👇 Copy Chapter

Download resources Copy Section

Write test code Copy Section

Setup CMakeLists.txt Copy Section

Build example Copy Section

Run binary: Copy Section

Supported Models Matrix Copy Chapter

NVIDIA GPU Inference: TensorRT Copy Section

CPU Inference: ONNXRuntime, MNN, NCNN and TNN Copy Section

Model Zoo. Copy Chapter

🔑️ How to download Model Zoo from Docker Hub? Copy Section

Model Hubs Copy Section

🔑️ More Examples. Copy Chapter

©️License Copy Chapter

🎉Contribute Copy Chapter

Explore Related Technologies & Topics

📖 News 🔥🔥

Citations 🎉🎉

Features 👏👋

Build 👇👇

Quick Start 🌟🌟

Quick Setup 👀

Mixed with MNN or ONNXRuntime 👇👇

Download resources

Write test code

Setup CMakeLists.txt

Build example

Run binary:

Supported Models Matrix

NVIDIA GPU Inference: TensorRT

CPU Inference: ONNXRuntime, MNN, NCNN and TNN

Model Zoo.

🔑️ How to download Model Zoo from Docker Hub?

Model Hubs

🔑️ More Examples.

©️License

🎉Contribute