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lite-ai-toolkit

🛠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 👇👇

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

Citations 🎉🎉

@misc{lite.ai.toolkit@2021,
  title={lite.ai.toolkit: A lite C++ toolkit of 100+ 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={DefTruth, 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:

git clone --depth=1 https://github.com/DefTruth/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-Zoo2.

#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;
}

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

export LITE_AI_TAG_URL=https://github.com/DefTruth/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

🎉🎉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.

// trtexec --onnx=yolov5s.onnx --saveEngine=yolov5s.engine
auto *yolov5 = new lite::trt::cv::detection::YOLOV5(engine_path);

Quick Setup 👀

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

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})

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:

#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 ...

The included headers of MNN and ONNXRuntime can be found at mnn_config.h and ort_config.h.

🔑️ Check the detailed Quick Start！Click here!

Download resources

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

export LITE_AI_TAG_URL=https://github.com/DefTruth/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

Write test code

write YOLOv5 example codes and name it test_lite_yolov5.cpp:

#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;
}

Setup CMakeLists.txt

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})

Build example

mkdir build && cd build && cmake .. && make -j1

Then, export the lib paths to LD_LIBRARY_PATH which listed by lite.ai.toolkit_LIBS_DIRS.

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

Run binary:

cp ../yolov5s.onnx ../test_yolov.jpg .
./lite_yolov5

The output logs:

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

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 / / 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 ✅ / / / / ✔️ ✔️ ❔
🔑️ Model Zoo！Click here!

Model Zoo.

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 
  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

🔑️ 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.

🔑️ More Examples！Click here!

🔑️ More 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 = "../../../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;
}

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
auto *detector = new lite::cv::detection::YOLOv6(onnx_path);  // Newest 2022 YOLO detector !!!

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

#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;
}

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.
auto *matting = new lite::cv::matting::MobileHumanMatting(onnx_path); // 3Mb only !!!

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

#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;
}

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 = "../../../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;
}

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 = "../../../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;
}

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 = "../../../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;
}

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!!
auto *detector = new lite::face::detect::YOLOv5BlazeFace(onnx_path);  // 2021, Super fast and accurate!!

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

#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;
}

The output is:

More classes for object segmentation (general objects 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 = "../../../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;
}

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 = "../../../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;
}

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 = "../../../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;
}

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 = "../../../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;
}

The output is:


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

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

Example11: Human Head Segmentation using HeadSeg. Download model from Model-Zoo2.

#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)

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

Example12: Photo transfer to Cartoon Photo2Cartoon. Download model from Model-Zoo2.

#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.

auto *transfer = new lite::cv::style::FemalePhoto2Cartoon(onnx_path);

Example13: Face Parsing using FaceParsing. Download model from Model-Zoo2.

#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)

auto *segment = new lite::cv::segmentation::FaceParsingBiSeNet(onnx_path); // 50Mb
auto *segment = new lite::cv::segmentation::FaceParsingBiSeNetDyn(onnx_path); // Dynamic Shape Inference.

©️License

GNU General Public License v3.0

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