deftruth / lite.ai.toolkit Goto Github PK

🛠 A lite C++ toolkit of awesome AI models, support ONNXRuntime, MNN. Contains YOLOv5, YOLOv6, YOLOX, YOLOR, FaceDet, HeadSeg, HeadPose, Matting etc. Engine: ONNXRuntime, MNN.

Home Page: https://github.com/DefTruth/lite.ai.toolkit

License: GNU General Public License v3.0

CMake 0.47% C++ 99.45% C 0.08% Shell 0.01%

yolox retinaface onnxruntime segmentation yolor yolop nanodet robustvideomatting mnn ncnn

lite.ai.toolkit's Introduction

🛠Lite.Ai.ToolKit: A lite C++ toolkit of awesome AI models, such as Object Detection, Face Detection, Face Recognition, Segmentation, Matting, etc. See Model Zoo and ONNX Hub, MNN Hub, TNN Hub, NCNN Hub.

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.

Other Repos 🔥🔥

🛠lite.ai.toolkit	💎torchlm	📒statistic-learning-R-note	🎉cuda-learn-note	📖Awesome-LLM-Inference

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

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.

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!

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.

Citations 🎉🎉

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

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lite.ai.toolkit's Issues

yolov5 代码问题请教

yolov5是一个基于anchor的算法，再前向推理的过程中应该涉及anchor的计算，但是在代码中没有看到andhor的任何信息，请问是怎么处理的？

模型都是怎么转换的？

我这边自定义训练的一个resnet数据模型是pt模型，怎么转onnx模型呢，我在您链接的vison里面没找到怎么转换？

Win10 VS2019遇到 LNK1104 无法打开文件“opencv_highgui.lib”

在windows上安装了OpenCV 4.5.4，并且设置好了路径，但是并没有生成opencv_highgui.lib库

Detectron2

Hi,
Thanks for sharing it. Could you please support the detectron2?

Thank you in advance

windows10 vs2019 编译错误

VS2019 windows 编译错误

VS2019 windows下，虽然下面有做枚举，但是这种应该它还没有识别：

Cannot use GPU on windows..

Hi again! I am running this on windows, and I see the following message:

2021-10-11 19:50:28.8453389 [W:onnxruntime:, fallback_cpu_capability.cc:135 onnxruntime::GetCpuPreferredNodes] Force fallback to CPU execution for node: Slice_339

Why is this happening? Should i be able to run this on the GPU?

Thanks!

RVM onyx models to Google drive?

Hi! thanks for making this code available. Is it possible to upload the rvm models to google drive? I am unable to access Baidu where i am. Thank you!

yolox_nano速度问题

我使用您的代码框架测试了一下yolox系列的推理速度，yolox_nano以外的模型推理速度都很正常，但是使用nano模型时，推理速度甚至低于yolox_s。所用的onnx文件均为利用官方coco数据集训练出来的pth文件转化得到。
我注意到yolox在定义nano模型时，有一段额外代码（./exps/default/nano.py中），如下图所示

这是否会有影响？

支持resnet50算法吗？

我转换的一个resnet50的算法，但是加载的时候，报错：
0x00007FF977504ED9 处(位于 xx.exe 中)引发的异常: Microsoft C++ 异常: std::length_error，位于内存位置 0x0000006D50B7EC30 处。
0x00007FF977504ED9 处(位于 xx.exe 中)有未经处理的异常: Microsoft C++ 异常: std::length_error，位于内存位置 0x0000006D50B7EC30 处。

How to use your toolkit with onnxruntime-gpu with Linux Ubuntu

Hello @DefTruth
Thank for your works. I tested some face detections in your toolkit and they work well base on CPU in Ubuntu 16.04
I would like to use GPU so I download onnxruntime-linux-x64-gpu-1.7.0.tgz
I followed your suggestion:
cp you-path-to-downloaded-or-built-onnxruntime/lib/onnxruntime lite.ai.toolkit/lib
and use the headers offer by this repo, I just let these directories unchanged only copy lib
but when I check the results so they did not run on GPU
Can you give me some suggestions?

contribute-lite.ai-cv-detection-template

model information: The information for the model is listed below.

Project Address	Author	Model File	Inference
yolov5 (🔥🔥💥↑)	ultralytics	yolov5-model-pytorch-hub	detect.py

Note: this is a template issue for how to contribute you models. Just replace the "template" as your model or project name, such as contribute-lite.ai-cv-detection-YoloV5 .

👉公告: Mac/Linux/Windows/Android预编译库计划

C++小白搞不定相互依赖的openvc等环境，求小白教程

How to build for windows 10 ? Many errors.

Hi I have a lot of errors when try to build on windows 10 with Qt (cmake and mingw64-make)

If there is precompiled DLL fow windows10 64 / 32 bit it would be perfect :)

windows vs2019编译报错：

core\ort_types.h(272,1): error C2440: “初始化”: 无法从“ortcv::types::BoundingBoxType<int,double>”转换为“ortcv::types::BoundingBoxType<int,float>”

Ubuntu下编译错误，怎么解决呢

ubuntu@ubuntu-M12SWA-TF:~/lite.ai.toolkit$ sh ./build.sh
build directory exist! clearing ...
clear built files done ! & rebuilding ...
-- The C compiler identification is GNU 7.5.0
-- The CXX compiler identification is GNU 7.5.0
-- Detecting C compiler ABI info
-- Detecting C compiler ABI info - done
-- Check for working C compiler: /usr/bin/cc - skipped
-- Detecting C compile features
-- Detecting C compile features - done
-- Detecting CXX compiler ABI info
-- Detecting CXX compiler ABI info - done
-- Check for working CXX compiler: /usr/bin/c++ - skipped
-- Detecting CXX compile features
-- Detecting CXX compile features - done
########## Checking Platform for: /home/ubuntu/lite.ai.toolkit ###########
==================================== Lite.AI.ToolKit 0.1.0 =============================
Project: lite.ai.toolkit
Version: 0.1.0
SO Version: 0.1.0
Build Type: MinSizeRel
Platform Name: linux
Root Path: /home/ubuntu/lite.ai.toolkit

################################### Engines Enable Details ... #######################################
-- INCLUDE_OPENCV: ON
-- ENABLE_ONNXRUNTIME: ON
-- ENABLE_MNN: OFF
-- ENABLE_NCNN: OFF
-- ENABLE_TNN: OFF
######################################################################################################
########## Setting up OpenCV libs for: /home/ubuntu/lite.ai.toolkit ###########
###########################################################################################
Installing Lite.AI.ToolKit Headers for ONNXRuntime Backend ...
-- Installing: /home/ubuntu/lite.ai.toolkit/build/lite.ai.toolkit/include/lite/ort/core/ort_config.h
··················
-- Configuring done
-- Generating done
-- Build files have been written to: /home/ubuntu/lite.ai.toolkit/build
[ 0%] Building CXX object CMakeFiles/lite.ai.toolkit.dir/lite/utils.cpp.o
[ 0%] Building CXX object CMakeFiles/lite.ai.toolkit.dir/lite/ort/cv/cava_ghost_arcface.cpp.o
[ 1%] Building CXX object CMakeFiles/lite.ai.toolkit.dir/lite/ort/cv/center_loss_face.cpp.o
[ 2%] Building CXX object CMakeFiles/lite.ai.toolkit.dir/lite/ort/cv/deeplabv3_resnet101.cpp.o
[ 2%] Building CXX object CMakeFiles/lite.ai.toolkit.dir/lite/ort/cv/colorizer.cpp.o
[ 3%] Building CXX object CMakeFiles/lite.ai.toolkit.dir/lite/ort/cv/age_googlenet.cpp.o
[ 3%] Building CXX object CMakeFiles/lite.ai.toolkit.dir/lite/ort/cv/cava_combined_face.cpp.o
[ 3%] Building CXX object CMakeFiles/lite.ai.toolkit.dir/lite/ort/cv/densenet.cpp.o
In file included from /home/ubuntu/lite.ai.toolkit/lite/utils.cpp:5:0:
/home/ubuntu/lite.ai.toolkit/lite/utils.h: In function ‘std::vector<_Tp> lite::utils::math::softmax(const T*, unsigned int, unsigned int&)’:
/home/ubuntu/lite.ai.toolkit/lite/utils.h:94:29: error: ‘expf’ is not a member of ‘std’
softmax_probs[i] = std::expf(logits[i]);
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.h:94:29: note: suggested alternative: ‘exp’
softmax_probs[i] = std::expf(logits[i]);
^~~~
exp
/home/ubuntu/lite.ai.toolkit/lite/utils.h: In function ‘std::vector<_Tp> lite::utils::math::softmax(const std::vector<_Tp>&, unsigned int&)’:
/home/ubuntu/lite.ai.toolkit/lite/utils.h:119:29: error: ‘expf’ is not a member of ‘std’
softmax_probs[i] = std::expf(logits[i]);
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.h:119:29: note: suggested alternative: ‘exp’
softmax_probs[i] = std::expf(logits[i]);
^~~~
exp
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp: In function ‘void lite::utils::draw_axis_inplace(cv::Mat&, const EulerAngles&, float, int)’:
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:48:47: error: ‘cosf’ is not a member of ‘std’
const int x1 = static_cast(size * std::cosf(yaw) * std::cosf(roll)) + tdx;
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:48:47: note: suggested alternative: ‘cosh’
const int x1 = static_cast(size * std::cosf(yaw) * std::cosf(roll)) + tdx;
^~~~
cosh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:48:64: error: ‘cosf’ is not a member of ‘std’
const int x1 = static_cast(size * std::cosf(yaw) * std::cosf(roll)) + tdx;
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:48:64: note: suggested alternative: ‘cosh’
const int x1 = static_cast(size * std::cosf(yaw) * std::cosf(roll)) + tdx;
^~~~
cosh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:50:35: error: ‘cosf’ is not a member of ‘std’
size * (std::cosf(pitch) * std::sinf(roll)
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:50:35: note: suggested alternative: ‘cosh’
size * (std::cosf(pitch) * std::sinf(roll)
^~~~
cosh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:50:54: error: ‘sinf’ is not a member of ‘std’
size * (std::cosf(pitch) * std::sinf(roll)
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:50:54: note: suggested alternative: ‘sinh’
size * (std::cosf(pitch) * std::sinf(roll)
^~~~
sinh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:51:37: error: ‘cosf’ is not a member of ‘std’
+ std::cosf(roll) * std::sinf(pitch) * std::sinf(yaw))
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:51:37: note: suggested alternative: ‘cosh’
+ std::cosf(roll) * std::sinf(pitch) * std::sinf(yaw))
^~~~
cosh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:51:55: error: ‘sinf’ is not a member of ‘std’
+ std::cosf(roll) * std::sinf(pitch) * std::sinf(yaw))
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:51:55: note: suggested alternative: ‘sinh’
+ std::cosf(roll) * std::sinf(pitch) * std::sinf(yaw))
^~~~
sinh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:51:74: error: ‘sinf’ is not a member of ‘std’
+ std::cosf(roll) * std::sinf(pitch) * std::sinf(yaw))
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:51:74: note: suggested alternative: ‘sinh’
+ std::cosf(roll) * std::sinf(pitch) * std::sinf(yaw))
^~~~
sinh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:54:48: error: ‘cosf’ is not a member of ‘std’
const int x2 = static_cast(-size * std::cosf(yaw) * std::sinf(roll)) + tdx;
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:54:48: note: suggested alternative: ‘cosh’
const int x2 = static_cast(-size * std::cosf(yaw) * std::sinf(roll)) + tdx;
^~~~
cosh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:54:65: error: ‘sinf’ is not a member of ‘std’
const int x2 = static_cast(-size * std::cosf(yaw) * std::sinf(roll)) + tdx;
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:54:65: note: suggested alternative: ‘sinh’
const int x2 = static_cast(-size * std::cosf(yaw) * std::sinf(roll)) + tdx;
^~~~
sinh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:56:35: error: ‘cosf’ is not a member of ‘std’
size * (std::cosf(pitch) * std::cosf(roll)
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:56:35: note: suggested alternative: ‘cosh’
size * (std::cosf(pitch) * std::cosf(roll)
^~~~
cosh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:56:54: error: ‘cosf’ is not a member of ‘std’
size * (std::cosf(pitch) * std::cosf(roll)
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:56:54: note: suggested alternative: ‘cosh’
size * (std::cosf(pitch) * std::cosf(roll)
^~~~
cosh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:57:37: error: ‘sinf’ is not a member of ‘std’
- std::sinf(pitch) * std::sinf(yaw) * std::sinf(roll))
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:57:37: note: suggested alternative: ‘sinh’
- std::sinf(pitch) * std::sinf(yaw) * std::sinf(roll))
^~~~
sinh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:57:56: error: ‘sinf’ is not a member of ‘std’
- std::sinf(pitch) * std::sinf(yaw) * std::sinf(roll))
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:57:56: note: suggested alternative: ‘sinh’
- std::sinf(pitch) * std::sinf(yaw) * std::sinf(roll))
^~~~
sinh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:57:73: error: ‘sinf’ is not a member of ‘std’
- std::sinf(pitch) * std::sinf(yaw) * std::sinf(roll))
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:57:73: note: suggested alternative: ‘sinh’
- std::sinf(pitch) * std::sinf(yaw) * std::sinf(roll))
^~~~
sinh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:60:47: error: ‘sinf’ is not a member of ‘std’
const int x3 = static_cast(size * std::sinf(yaw)) + tdx;
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:60:47: note: suggested alternative: ‘sinh’
const int x3 = static_cast(size * std::sinf(yaw)) + tdx;
^~~~
sinh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:61:48: error: ‘cosf’ is not a member of ‘std’
const int y3 = static_cast(-size * std::cosf(yaw) * std::sinf(pitch)) + tdy;
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:61:48: note: suggested alternative: ‘cosh’
const int y3 = static_cast(-size * std::cosf(yaw) * std::sinf(pitch)) + tdy;
^~~~
cosh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:61:65: error: ‘sinf’ is not a member of ‘std’
const int y3 = static_cast(-size * std::cosf(yaw) * std::sinf(pitch)) + tdy;
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:61:65: note: suggested alternative: ‘sinh’
const int y3 = static_cast(-size * std::cosf(yaw) * std::sinf(pitch)) + tdy;
^~~~
sinh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp: In function ‘cv::Mat lite::utils::draw_axis(const cv::Mat&, const EulerAngles&, float, int)’:
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:86:47: error: ‘cosf’ is not a member of ‘std’
const int x1 = static_cast(size * std::cosf(yaw) * std::cosf(roll)) + tdx;
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:86:47: note: suggested alternative: ‘cosh’
const int x1 = static_cast(size * std::cosf(yaw) * std::cosf(roll)) + tdx;
^~~~
cosh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:86:64: error: ‘cosf’ is not a member of ‘std’
const int x1 = static_cast(size * std::cosf(yaw) * std::cosf(roll)) + tdx;
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:86:64: note: suggested alternative: ‘cosh’
const int x1 = static_cast(size * std::cosf(yaw) * std::cosf(roll)) + tdx;
^~~~
cosh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:88:35: error: ‘cosf’ is not a member of ‘std’
size * (std::cosf(pitch) * std::sinf(roll)
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:88:35: note: suggested alternative: ‘cosh’
size * (std::cosf(pitch) * std::sinf(roll)
^~~~
cosh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:88:54: error: ‘sinf’ is not a member of ‘std’
size * (std::cosf(pitch) * std::sinf(roll)
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:88:54: note: suggested alternative: ‘sinh’
size * (std::cosf(pitch) * std::sinf(roll)
^~~~
sinh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:89:37: error: ‘cosf’ is not a member of ‘std’
+ std::cosf(roll) * std::sinf(pitch) * std::sinf(yaw))
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:89:37: note: suggested alternative: ‘cosh’
+ std::cosf(roll) * std::sinf(pitch) * std::sinf(yaw))
^~~~
cosh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:89:55: error: ‘sinf’ is not a member of ‘std’
+ std::cosf(roll) * std::sinf(pitch) * std::sinf(yaw))
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:89:55: note: suggested alternative: ‘sinh’
+ std::cosf(roll) * std::sinf(pitch) * std::sinf(yaw))
^~~~
sinh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:89:74: error: ‘sinf’ is not a member of ‘std’
+ std::cosf(roll) * std::sinf(pitch) * std::sinf(yaw))
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:89:74: note: suggested alternative: ‘sinh’
+ std::cosf(roll) * std::sinf(pitch) * std::sinf(yaw))
^~~~
sinh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:92:48: error: ‘cosf’ is not a member of ‘std’
const int x2 = static_cast(-size * std::cosf(yaw) * std::sinf(roll)) + tdx;
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:92:48: note: suggested alternative: ‘cosh’
const int x2 = static_cast(-size * std::cosf(yaw) * std::sinf(roll)) + tdx;
^~~~
cosh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:92:65: error: ‘sinf’ is not a member of ‘std’
const int x2 = static_cast(-size * std::cosf(yaw) * std::sinf(roll)) + tdx;
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:92:65: note: suggested alternative: ‘sinh’
const int x2 = static_cast(-size * std::cosf(yaw) * std::sinf(roll)) + tdx;
^~~~
sinh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:94:35: error: ‘cosf’ is not a member of ‘std’
size * (std::cosf(pitch) * std::cosf(roll)
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:94:35: note: suggested alternative: ‘cosh’
size * (std::cosf(pitch) * std::cosf(roll)
^~~~
cosh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:94:54: error: ‘cosf’ is not a member of ‘std’
size * (std::cosf(pitch) * std::cosf(roll)
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:94:54: note: suggested alternative: ‘cosh’
size * (std::cosf(pitch) * std::cosf(roll)
^~~~
cosh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:95:37: error: ‘sinf’ is not a member of ‘std’
- std::sinf(pitch) * std::sinf(yaw) * std::sinf(roll))
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:95:37: note: suggested alternative: ‘sinh’
- std::sinf(pitch) * std::sinf(yaw) * std::sinf(roll))
^~~~
sinh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:95:56: error: ‘sinf’ is not a member of ‘std’
- std::sinf(pitch) * std::sinf(yaw) * std::sinf(roll))
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:95:56: note: suggested alternative: ‘sinh’
- std::sinf(pitch) * std::sinf(yaw) * std::sinf(roll))
^~~~
sinh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:95:73: error: ‘sinf’ is not a member of ‘std’
- std::sinf(pitch) * std::sinf(yaw) * std::sinf(roll))
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:95:73: note: suggested alternative: ‘sinh’
- std::sinf(pitch) * std::sinf(yaw) * std::sinf(roll))
^~~~
sinh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:98:47: error: ‘sinf’ is not a member of ‘std’
const int x3 = static_cast(size * std::sinf(yaw)) + tdx;
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:98:47: note: suggested alternative: ‘sinh’
const int x3 = static_cast(size * std::sinf(yaw)) + tdx;
^~~~
sinh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:99:48: error: ‘cosf’ is not a member of ‘std’
const int y3 = static_cast(-size * std::cosf(yaw) * std::sinf(pitch)) + tdy;
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:99:48: note: suggested alternative: ‘cosh’
const int y3 = static_cast(-size * std::cosf(yaw) * std::sinf(pitch)) + tdy;
^~~~
cosh
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:99:65: error: ‘sinf’ is not a member of ‘std’
const int y3 = static_cast(-size * std::cosf(yaw) * std::sinf(pitch)) + tdy;
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:99:65: note: suggested alternative: ‘sinh’
const int y3 = static_cast(-size * std::cosf(yaw) * std::sinf(pitch)) + tdy;
^~~~
sinh
In file included from /home/ubuntu/lite.ai.toolkit/lite/ort/cv/deeplabv3_resnet101.cpp:6:0:
/home/ubuntu/lite.ai.toolkit/lite/ort/core/ort_utils.h:33:80: warning: dynamic exception specifications are deprecated in C++11 [-Wdeprecated]
unsigned int data_format = CHW) throw(std::runtime_error);
^~~~~
In file included from /home/ubuntu/lite.ai.toolkit/lite/ort/cv/densenet.cpp:6:0:
/home/ubuntu/lite.ai.toolkit/lite/ort/core/ort_utils.h:33:80: warning: dynamic exception specifications are deprecated in C++11 [-Wdeprecated]
unsigned int data_format = CHW) throw(std::runtime_error);
^~~~~
In file included from /home/ubuntu/lite.ai.toolkit/lite/ort/cv/densenet.cpp:7:0:
/home/ubuntu/lite.ai.toolkit/lite/utils.h: In function ‘std::vector<_Tp> lite::utils::math::softmax(const T*, unsigned int, unsigned int&)’:
/home/ubuntu/lite.ai.toolkit/lite/utils.h:94:29: error: ‘expf’ is not a member of ‘std’
softmax_probs[i] = std::expf(logits[i]);
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.h:94:29: note: suggested alternative: ‘exp’
softmax_probs[i] = std::expf(logits[i]);
^~~~
exp
/home/ubuntu/lite.ai.toolkit/lite/utils.h: In function ‘std::vector<_Tp> lite::utils::math::softmax(const std::vector<_Tp>&, unsigned int&)’:
/home/ubuntu/lite.ai.toolkit/lite/utils.h:119:29: error: ‘expf’ is not a member of ‘std’
softmax_probs[i] = std::expf(logits[i]);
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.h:119:29: note: suggested alternative: ‘exp’
softmax_probs[i] = std::expf(logits[i]);
^~~~
exp
In file included from /home/ubuntu/lite.ai.toolkit/lite/ort/cv/colorizer.cpp:6:0:
/home/ubuntu/lite.ai.toolkit/lite/ort/core/ort_utils.h:33:80: warning: dynamic exception specifications are deprecated in C++11 [-Wdeprecated]
unsigned int data_format = CHW) throw(std::runtime_error);
^~~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp: In function ‘void lite::utils::blending_nms(std::vector<lite::types::BoundingBoxType<float, float> >&, std::vector<lite::types::BoundingBoxType<float, float> >&, float, unsigned int)’:
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:327:21: error: ‘expf’ is not a member of ‘std’
total += std::expf(buf[k].score);
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:327:21: note: suggested alternative: ‘exp’
total += std::expf(buf[k].score);
^~~~
exp
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:332:25: error: ‘expf’ is not a member of ‘std’
float rate = std::expf(buf[l].score) / total;
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.cpp:332:25: note: suggested alternative: ‘exp’
float rate = std::expf(buf[l].score) / total;
^~~~
exp
In file included from /home/ubuntu/lite.ai.toolkit/lite/ort/cv/cava_combined_face.cpp:6:0:
/home/ubuntu/lite.ai.toolkit/lite/ort/core/ort_utils.h:33:80: warning: dynamic exception specifications are deprecated in C++11 [-Wdeprecated]
unsigned int data_format = CHW) throw(std::runtime_error);
^~~~~
In file included from /home/ubuntu/lite.ai.toolkit/lite/ort/cv/cava_combined_face.cpp:7:0:
/home/ubuntu/lite.ai.toolkit/lite/utils.h: In function ‘std::vector<_Tp> lite::utils::math::softmax(const T*, unsigned int, unsigned int&)’:
/home/ubuntu/lite.ai.toolkit/lite/utils.h:94:29: error: ‘expf’ is not a member of ‘std’
softmax_probs[i] = std::expf(logits[i]);
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.h:94:29: note: suggested alternative: ‘exp’
softmax_probs[i] = std::expf(logits[i]);
^~~~
exp
/home/ubuntu/lite.ai.toolkit/lite/utils.h: In function ‘std::vector<_Tp> lite::utils::math::softmax(const std::vector<_Tp>&, unsigned int&)’:
/home/ubuntu/lite.ai.toolkit/lite/utils.h:119:29: error: ‘expf’ is not a member of ‘std’
softmax_probs[i] = std::expf(logits[i]);
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.h:119:29: note: suggested alternative: ‘exp’
softmax_probs[i] = std::expf(logits[i]);
^~~~
exp
In file included from /home/ubuntu/lite.ai.toolkit/lite/ort/cv/center_loss_face.cpp:6:0:
/home/ubuntu/lite.ai.toolkit/lite/ort/core/ort_utils.h:33:80: warning: dynamic exception specifications are deprecated in C++11 [-Wdeprecated]
unsigned int data_format = CHW) throw(std::runtime_error);
^~~~~
In file included from /home/ubuntu/lite.ai.toolkit/lite/ort/cv/age_googlenet.cpp:5:0:
/home/ubuntu/lite.ai.toolkit/lite/ort/core/ort_utils.h:33:80: warning: dynamic exception specifications are deprecated in C++11 [-Wdeprecated]
unsigned int data_format = CHW) throw(std::runtime_error);
^~~~~
In file included from /home/ubuntu/lite.ai.toolkit/lite/ort/cv/age_googlenet.cpp:6:0:
/home/ubuntu/lite.ai.toolkit/lite/utils.h: In function ‘std::vector<_Tp> lite::utils::math::softmax(const T*, unsigned int, unsigned int&)’:
/home/ubuntu/lite.ai.toolkit/lite/utils.h:94:29: error: ‘expf’ is not a member of ‘std’
softmax_probs[i] = std::expf(logits[i]);
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.h:94:29: note: suggested alternative: ‘exp’
softmax_probs[i] = std::expf(logits[i]);
^~~~
exp
/home/ubuntu/lite.ai.toolkit/lite/utils.h: In function ‘std::vector<_Tp> lite::utils::math::softmax(const std::vector<_Tp>&, unsigned int&)’:
/home/ubuntu/lite.ai.toolkit/lite/utils.h:119:29: error: ‘expf’ is not a member of ‘std’
softmax_probs[i] = std::expf(logits[i]);
^~~~
/home/ubuntu/lite.ai.toolkit/lite/utils.h:119:29: note: suggested alternative: ‘exp’
softmax_probs[i] = std::expf(logits[i]);
^~~~
exp
In file included from /home/ubuntu/lite.ai.toolkit/lite/ort/cv/cava_ghost_arcface.cpp:6:0:
/home/ubuntu/lite.ai.toolkit/lite/ort/core/ort_utils.h:33:80: warning: dynamic exception specifications are deprecated in C++11 [-Wdeprecated]
unsigned int data_format = CHW) throw(std::runtime_error);
^~~~~
CMakeFiles/lite.ai.toolkit.dir/build.make:75: recipe for target 'CMakeFiles/lite.ai.toolkit.dir/lite/utils.cpp.o' failed
make[2]: *** [CMakeFiles/lite.ai.toolkit.dir/lite/utils.cpp.o] Error 1
make[2]: *** 正在等待未完成的任务....
CMakeFiles/lite.ai.toolkit.dir/build.make:173: recipe for target 'CMakeFiles/lite.ai.toolkit.dir/lite/ort/cv/densenet.cpp.o' failed
make[2]: *** [CMakeFiles/lite.ai.toolkit.dir/lite/ort/cv/densenet.cpp.o] Error 1
CMakeFiles/lite.ai.toolkit.dir/build.make:103: recipe for target 'CMakeFiles/lite.ai.toolkit.dir/lite/ort/cv/cava_combined_face.cpp.o' failed
make[2]: *** [CMakeFiles/lite.ai.toolkit.dir/lite/ort/cv/cava_combined_face.cpp.o] Error 1
CMakeFiles/lite.ai.toolkit.dir/build.make:89: recipe for target 'CMakeFiles/lite.ai.toolkit.dir/lite/ort/cv/age_googlenet.cpp.o' failed
make[2]: *** [CMakeFiles/lite.ai.toolkit.dir/lite/ort/cv/age_googlenet.cpp.o] Error 1
CMakeFiles/Makefile2:237: recipe for target 'CMakeFiles/lite.ai.toolkit.dir/all' failed
make[1]: *** [CMakeFiles/lite.ai.toolkit.dir/all] Error 2
Makefile:90: recipe for target 'all' failed
make: *** [all] Error 2

tnn编译 tnn_rvm错误

windows vs2019

有计划支持 linux 吗？

yolox in tnn???

do you have the tnn code of yolox?

看看这个，挺有意思，集成进来？

https://github.com/ibaiGorordo/ONNX-Mobile-Human-Pose-3D

有没有QQ群，什么的？有问题好交流

关于人脸，在68或者98或者1000点对齐后，有没有人脸对齐相关的函数？

就是说，人脸的68或者98或者1000点检测，对侧脸能很好低检测吗？

Running models in half precision FP16

I am trying to run the FP16 version of the model "rvm_mobilenetv3_fp16.onnx"

I am trying to write a FP16 version of the helper function
Ort::Value ortcv::utils::transform::create_tensor()

I understand I have to use the function:
inline Value Value::CreateTensor(const OrtMemoryInfo* info, void* p_data, size_t p_data_byte_count, const int64_t* shape, size_t shape_len, ONNXTensorElementDataType type)

With ONNXTensorElementDataType = ONNX_TENSOR_ELEMENT_DATA_TYPE_BFLOAT16 // Non-IEEE floating-point format based on IEEE754 single-precision

But i am stuck working out how to handle half resolution vectors in c++

Probably it is necessary to handle uint16 types to handle the pointers and make some conversion to half floats at some point, but I am lost about how to handle this.

big bug!发现了一个大bug

比如，在yolov5里面，推理时，预处理为，直接把图像缩放到输入大小，比如640*640，这样会导致很多图像扭曲变形，导致识别不准确：
Ort::Value YoloV5::transform(const cv::Mat &mat)
{
cv::Mat canva = mat.clone();
cv::cvtColor(canva, canva, cv::COLOR_BGR2RGB);
cv::resize(canva, canva, cv::Size(input_node_dims.at(3),
input_node_dims.at(2)));
// (1,3,640,640) 1xCXHXW

ortcv::utils::transform::normalize_inplace(canva, mean_val, scale_val); // float32
return ortcv::utils::transform::create_tensor(
canva, input_node_dims, memory_info_handler,
input_values_handler, ortcv::utils::transform::CHW);
}

而在python里面的代码，确是求的一个最小缩放比例，然后把原图安装缩放比例缩放，然后进行不够640的，补边处理，这样就不会对图像里面的进行不等比例缩放，图像不会扭曲；还原识别框的时候，进行反操作，这样的：

def letterbox(img, new_shape=(416, 416), color=(114, 114, 114), auto=False, scaleFill=False, scaleup=True):
shape = img.shape[:2] # current shape [height, width]
if isinstance(new_shape, int):
new_shape = (new_shape, new_shape)

r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
if not scaleup:
    r = min(r, 1.0)

ratio = r, r  # width, height ratios
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
if auto:  # minimum rectangle
    dw, dh = np.mod(dw, 64), np.mod(dh, 64)  # wh padding
elif scaleFill:  # stretch
    dw, dh = 0.0, 0.0
    new_unpad = (new_shape[1], new_shape[0])
    ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios

dw /= 2  # divide padding into 2 sides
dh /= 2
if shape[::-1] != new_unpad:  # resize
    img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
return img, ratio, (dw, dh)

def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
# Rescale coords (xyxy) from img1_shape to img0_shape
if ratio_pad is None: # calculate from img0_shape
gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new
pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding
else:
gain = ratio_pad[0][0]
pad = ratio_pad[1]

coords[:, [0, 2]] -= pad[0]  # x padding
coords[:, [1, 3]] -= pad[1]  # y padding
coords[:, :4] /= gain
clip_coords(coords, img0_shape)
return coords

VS015 编译报错

vs2019时正常，但是，项目需要vs2015，改成VS015活，编译报错，貌似对模板什么的支持不好

efficientdet 推理好慢

gpu推理，3秒多：

Not working under windows 10

Hello,

I am trying to test it under Windows 10 with Visual Studio 2019.
It compiles fine, but it always gives me the same error :
the application was unable to start correctly 0xc000007b.
Already tried with Release, Debug, MinSizeRel and RelWithDebInfo.
Could you please help me ?

Thank you.

无法运行example里面的test yolox

报错了

错误 LNK2001 无法解析的外部符号 "public: void __cdecl ortcv::YoloX::detect(class cv::Mat const &,class std::vector<struct ortcv::types::BoundingBoxType<float,float>,class std::allocator<struct ortcv::types::BoundingBoxType<float,float> > > &,float,float,unsigned int,unsigned int)" (?detect@YoloX@ortcv@@QEAAXAEBVMat@cv@@aeav?$vector@U?$BoundingBoxType@MM@types@ortcv@@v?$allocator@U?$BoundingBoxType@MM@types@ortcv@@@std@@@std@@mmii@Z) gyy_ort_test D:\Download\lite.ai-main\gyy_test\gyy_ort_test\gyy_ort_test\source.obj 1
请问这种情况应该怎么解决啊

库文件.dylib时什么格式？

onnxruntime问题请教

Ort::Env m_env;
Ort::Session m_session;
请问这两个关系是怎么样的，之前看onnxruntime的文档介绍，Ort::Env是一个全局唯一的，如果要实现一个生产者消费者的推理模块来扩大推理引擎的并发性，是不是所有线程共用一个Ort::Env，每个消费者线程新建一个Ort::Session对象？麻烦不吝指教

请问一下，yolov5 支持5.0版本吗？

demo中，默认模式就是onnxruntime模式？

BoundingBoxType的label_text想改成string类型，但是很多报错

想自己给label_text赋值string，然后发现label_text是为NULL，没法赋值，所以想把label_text改为string

onnxruntime的gpu怎么支持？

Using onnxruntime iobinding to eliminate useless transfers

It would be great to be able to use onnxruntime iobinding to eliminate useless transfers to improve the performance.

For the RobustVideoMatting network the author explains how use the iobinding in python to keep the tensors of the recurrent states in the GPU (avoiding copying them to the CPU and back to the GPU in the next frame):
https://github.com/PeterL1n/RobustVideoMatting/blob/master/documentation/inference.md

I've been looking for information to implement this iobinding in c++ but I couldn't find any reference.

I've figured out how to get the iobinding object from a pointer of the session:
ort_iobinding = new Ort::IoBinding(*ort_session);

To bind the outputs there are to methods:
void BindOutput(const char* name, const Value&);
void BindOutput(const char* name, const MemoryInfo&);

I think the correct way would be to create a MemoryInfo for CUDA device, but not sure if the following way would be correct:
Ort::MemoryInfo info_cuda("Cuda", OrtAllocatorType::OrtArenaAllocator, 0, OrtMemTypeDefault);
for (int i = 0; i < num_outputs; i++) ort_iobinding->BindOutput(output_node_names[i], info_cuda);

To bind the outputs there is only one method:
void BindInput(const char* name, const Value&);

To create the Ort::Value for the "src" input and bind it I think we can do:
Ort::Value srcTensor = Ort::Value::CreateTensor(memory_info_handler, src_values.data(), src_size, src_dims.data(), src_dims.size());
ort_iobinding->BindInput(input_node_names[0], srcTensor);

But I haven't been able to figure out how to create the tensors for the recurrent states as CUDA data.

I've tried the following code putting everything in the CPU just to check it works:

-Once at the begining:
ort_iobinding = new Ort::IoBinding(*ort_session);
for (int i = 0; i < num_outputs; i++) ort_iobinding->BindOutput(output_node_names[i], memory_info_handler);

Every frame:
Ort::Value srcTensor = Ort::Value::CreateTensor(memory_info_handler, src_values.data(), src_size, src_dims.data(), src_dims.size());
Ort::Value r1iTensor = Ort::Value::CreateTensor(memory_info_handler, r1i_values.data(), r1i_size, r1i_dims.data(), r1i_dims.size());
Ort::Value r2iTensor = Ort::Value::CreateTensor(memory_info_handler, r2i_values.data(), r2i_size, r2i_dims.data(), r2i_dims.size());
Ort::Value r3iTensor = Ort::Value::CreateTensor(memory_info_handler, r3i_values.data(), r3i_size, r3i_dims.data(), r3i_dims.size());
Ort::Value r4iTensor = Ort::Value::CreateTensor(memory_info_handler, r4i_values.data(), r4i_size, r4i_dims.data(), r4i_dims.size());
Ort::Value dsrTensor = Ort::Value::CreateTensor(memory_info_handler, dsr_values.data(), dsr_size, dsr_dims.data(), dsr_dims.size());
ort_iobinding->BindInput(input_node_names[0], srcTensor);
ort_iobinding->BindInput(input_node_names[1], r1iTensor);
ort_iobinding->BindInput(input_node_names[2], r2iTensor);
ort_iobinding->BindInput(input_node_names[3], r3iTensor);
ort_iobinding->BindInput(input_node_names[4], r4iTensor);
ort_iobinding->BindInput(input_node_names[5], dsrTensor);
ort_session->Run(Ort::RunOptions{ nullptr }, *ort_iobinding);
auto output_tensors = ort_iobinding->GetOutputValues();

And it works correctly, but obviously at the same performance. The point would be to figure out how to keep the tensors of the recurrent states always in the GPU memory.

yolox更新问题

自从yolox更新之后似乎不再适用新版模型了请问哪里需要进行调整呢

有没有计划，集成一下Unet?

在win10上怎么部署、测试？

ort_types.h Ubuntu gcc5.4 编译错误

ort_types.h:279:64: error: conversion from ‘ortcv::types::BoundingBoxType<int, double>’ to non-scalar type ‘ortcv::types::BoundingBoxType<int, float>’ requested
  BoundingBoxType<int> boxi = this->template convert_type<int>();

请问大概是什么原因？

头文件包含问题

我另外一个工程要调用lite.ai.toolkit库时，我仍然需要包含onnxruntime mnn和ncnn库的头文件，其实，调用的时候，我只需要接口即可，可不可以尽量把onnxruntime mnn和ncnn库头文件目录的包含只限制到lite.ai.toolkit里面，其他程序调用lite.ai.toolkit时，不需要包含onnxruntime mnn和ncnn库头文件？？

yolov5 代码问题请教

yolov5是一个基于anchor的算法，再前向推理的过程中应该涉及anchor的计算，但是在代码中没有看到andhor的任何信息，请问是怎么处理的？

Access to models

Hi,

Thanks for your work. Could you please share the models on GDrive? because I can't access Baidu.

Thanks

使用gpu跑2个分割网络，都得9s多，有点慢

Runtime Version Detected Sim always same even changed the person

Hi,

I succesully compiled on the Mac Os x.

trying face rec. algorithms and recognized that ONNX
Runtime Version Detected Sim always same even changed the person.

ie : lite_glint_arcface.cpp
model : std::string onnx_path = "../../../hub/onnx/cv/ms1mv3_arcface_r100.onnx";

person a - person b
:

/var/folders/h6/7d637725049b0nf7_xqjkf640000gn/T/tmpl3pFGJ ; exit;
LITEORT_DEBUG LogId: ../../../hub/onnx/cv/ms1mv3_arcface_r100.onnx
=============== Input-Dims ==============
input_node_dims: 1
input_node_dims: 3
input_node_dims: 112
input_node_dims: 112
=============== Output-Dims ==============
Output: 0 Name: embedding Dim: 0 :1
Output: 0 Name: embedding Dim: 1 :512
[ WARN:0] global /Users/yanjunqiu/Desktop/third_party/library/opencv/modules/core/src/matrix_expressions.cpp (1334) assign OpenCV/MatExpr: processing of multi-channel arrays might be changed in the future: opencv/opencv#16739
Default Version Detected Sim: 0.415043
Default Version Detected Dist: 1.08163
LITEORT_DEBUG LogId: ../../../hub/onnx/cv/ms1mv3_arcface_r100.onnx
=============== Input-Dims ==============
input_node_dims: 1
input_node_dims: 3
input_node_dims: 112
input_node_dims: 112
=============== Output-Dims ==============
Output: 0 Name: embedding Dim: 0 :1
Output: 0 Name: embedding Dim: 1 :512
ONNXRuntime Version Detected Sim: 0.0349244

person-x personc

/var/folders/h6/7d637725049b0nf7_xqjkf640000gn/T/tmpzFKmvz ; exit;
LITEORT_DEBUG LogId: ../../../hub/onnx/cv/ms1mv3_arcface_r100.onnx
=============== Input-Dims ==============
input_node_dims: 1
input_node_dims: 3
input_node_dims: 112
input_node_dims: 112
=============== Output-Dims ==============
Output: 0 Name: embedding Dim: 0 :1
Output: 0 Name: embedding Dim: 1 :512
[ WARN:0] global /Users/yanjunqiu/Desktop/third_party/library/opencv/modules/core/src/matrix_expressions.cpp (1334) assign OpenCV/MatExpr: processing of multi-channel arrays might be changed in the future: opencv/opencv#16739
Default Version Detected Sim: 0.0609607
Default Version Detected Dist: 1.37043
LITEORT_DEBUG LogId: ../../../hub/onnx/cv/ms1mv3_arcface_r100.onnx
=============== Input-Dims ==============
input_node_dims: 1
input_node_dims: 3
input_node_dims: 112
input_node_dims: 112
=============== Output-Dims ==============
Output: 0 Name: embedding Dim: 0 :1
Output: 0 Name: embedding Dim: 1 :512
ONNXRuntime Version Detected Sim: 0.0349244

YoloR可以支持一下不？

MG Matting

Hi! Do you have any plans to implement mask guiding matting into this toolkit?

https://github.com/yucornetto/MGMatting

It would be amazing to run it through c++!

Thanks.

deftruth / lite.ai.toolkit Goto Github PK

lite.ai.toolkit's Introduction

Features 👏👋

Other Repos 🔥🔥

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Quick Start 🌟🌟

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

Quick Setup 👀

Mixed with MNN or ONNXRuntime 👇👇

Download resources

Write test code

Setup CMakeLists.txt

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Supported Models Matrix

Model Zoo.

🔑️ How to download Model Zoo from Docker Hub?

Model Hubs

Examples.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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