# lite.ai.toolkit **Repository Path**: naoano/lite.ai.toolkit ## Basic Information - **Project Name**: lite.ai.toolkit - **Description**: https://github.com/DefTruth/lite.ai.toolkit - **Primary Language**: Unknown - **License**: GPL-3.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2023-09-05 - **Last Updated**: 2023-09-06 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

![logo-v3](docs/resources/logo-v3.png)

🛠**Lite.Ai.ToolKit**: 一个轻量级的`C++` AI模型工具箱，用户友好（还行吧），开箱即用。已经包括 **[100+](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.onnx.md)** 流行的开源模型。这是一个根据个人兴趣整理的C++工具箱，, 涵盖[目标检测](#lite.ai.toolkit-object-detection)、[人脸检测](#lite.ai.toolkit-face-detection)、[人脸识别](#lite.ai.toolkit-face-recognition)、[语义分割](#lite.ai.toolkit-segmentation)、[抠图](#lite.ai.toolkit-matting)等领域。详见 [Model Zoo](#lite.ai.toolkit-Model-Zoo) 和 [ONNX Hub](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.onnx.md) 、[MNN Hub](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.mnn.md) 、[TNN Hub](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.tnn.md) 、[NCNN Hub](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.ncnn.md). [若是有用，❤️不妨给个⭐️🌟支持一下吧，感谢支持~]

English | 中文文档 | MacOS | Linux | Windows

## 重要通知 (Important Note) !!! 🔥🔥🔥 中文简体：本项目将不再频繁更新，更优的部署体验请尝试[⚡️PaddlePaddle/FastDeploy](https://github.com/PaddlePaddle/FastDeploy) : ⚡️一款简单易用的推理部署工具箱。覆盖业界主流优质预训练模型并提供开箱即用的开发体验，包括图像分类、目标检测、图像分割、人脸检测、人体关键点识别、文字识别等多任务，满足开发者多场景，多硬件、多平台的快速部署需求，并同时支持 **C++** 和 **Python** 两种语言。**lite.ai.toolkit** 中的核心模型未来将会以contrib的方式集成到[⚡️PaddlePaddle/FastDeploy](https://github.com/PaddlePaddle/FastDeploy) 中。欢迎同学们使用 ✋👉[⚡️PaddlePaddle/FastDeploy](https://github.com/PaddlePaddle/FastDeploy). ---- English: This project will no longer be updated frequently. For a better deployment experience, please try [⚡️PaddlePaddle/FastDeploy](https://github.com/PaddlePaddle/FastDeploy): ⚡️An Easy-to-use and Fast Deep Learning Model Deployment Toolkit. Covering the industry's mainstream high-quality pre-training models and providing out-of-the-box development experience, including image classification, object detection, image segmentation, face detection, human key point detection, text recognition and so on, to meet the needs of developers in multiple scenarios and multiple hardware, multi-platform deployment requirements. Furthermore, FastDeploy supports both **C++** and **Python** languages. The core models in **lite.ai.toolkit** will be integrated into [⚡️PaddlePaddle/FastDeploy](https://github.com/PaddlePaddle/FastDeploy) in a contrib way in the future. Welcome to use ✋👉[⚡️PaddlePaddle/FastDeploy](https://github.com/PaddlePaddle/FastDeploy). ---- - [⚡️PaddlePaddle/FastDeploy](https://github.com/PaddlePaddle/FastDeploy) 近期更新 🔥🔥🔥 - 🔥 **2022.8.18：发布FastDeploy [release/v0.2.0](https://github.com/PaddlePaddle/FastDeploy/)** - **服务端全新升级：一套SDK，覆盖全量模型** - 发布基于x86 CPU、NVIDIA GPU的易用、高性能推理引擎SDK，推理速度大幅提升 - 支持ONNXRuntime、Paddle Inference、TensorRT推理引擎 - 支持YOLOv7、YOLOv6、YOLOv5、PP-YOLOE等目标检测最优模型及[Demo示例](https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples/vision/detection/) - 支持人脸检测、人脸识别、实时人像抠图、图像分割等40+重点模型及[Demo示例](https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples/vision/) - 支持Python API 和 C++ API - 开发AI模型部署代码量减少～60% - **端侧继ARM CPU后，延伸至瑞芯微、晶晨、恩智浦等NPU能力** - 发布轻量化目标检测 [Picodet-NPU部署Demo](https://github.com/PaddlePaddle/Paddle-Lite-Demo/tree/develop/object_detection/linux/picodet_detection) ，提供低门槛INT8全量化能力 - **同时支持Python和C++两种语言** - 支持 `pip install` 一行命令安装，Linux/Mac/Windows 快速使用 - 提供 Linux/Mac/Windows，CPU/GPU 等多平台多硬件支持的C++预编译库 - 更多详情请跳转✋👉[⚡️PaddlePaddle/FastDeploy](https://github.com/PaddlePaddle/FastDeploy) 🎉🎉 ---- ## 核心特征👏👋

* **用户友好，开箱即用。** 使用简单一致的调用语法，如**lite::cv::Type::Class**，详见[examples](#lite.ai.toolkit-Examples-for-Lite.AI.ToolKit). * **少量依赖，构建容易。** 目前, 默认只依赖 **OpenCV** 和 **ONNXRuntime**，详见[build](#lite.ai.toolkit-Build-Lite.AI.ToolKit)。 * **众多的算法模块，且持续更新。** 目前，包括将近 **[300+](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.onnx.md)** C++实现以及 **[500+](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.onnx.md)** 权重文件 ## 引用 🎉🎉 如果您在自己的项目中使用了**Lite.Ai.ToolKit**，可考虑按以下方式进行引用。 ```BibTeX @misc{lite.ai.toolkit2021, title={lite.ai.toolkit: A lite C++ toolkit of awesome AI models.}, url={https://github.com/DefTruth/lite.ai.toolkit}, note={Open-source software available at https://github.com/DefTruth/lite.ai.toolkit}, author={Yan Jun}, year={2021} } ``` ## 关于训练 🤓👀 一个用于人脸关键点检测的训练和评估的工具箱已经开源，可通过pip一键安装，地址在[torchlm](https://github.com/DefTruth/torchlm). ## 预编译库和技术规划 ✅ ![](docs/resources/lite.ai.toolkit-roadmap-v0.1.png) 目前，有一些预编译的MacOS(x64)和Linux(x64)下的lite.ai.toolkit动态库，可以直接从以下链接进行下载。Windows(x64)和Android下的预编译库，也会在最近发布出来。更多详情请参考[issues#48](https://github.com/DefTruth/lite.ai.toolkit/issues/48) . 更多可下载的的预编译库，请跳转到[releases](https://github.com/DefTruth/lite.ai.toolkit/releases) 查看。 * [x] [lite0.1.1-osx10.15.x-ocv4.5.2-ffmpeg4.2.2-onnxruntime1.8.1.zip](https://github.com/DefTruth/lite.ai.toolkit/releases/download/v0.1.1/lite0.1.1-osx10.15.x-ocv4.5.2-ffmpeg4.2.2-onnxruntime1.8.1.zip) * [x] [lite0.1.1-osx10.15.x-ocv4.5.2-ffmpeg4.2.2-onnxruntime1.9.0.zip](https://github.com/DefTruth/lite.ai.toolkit/releases/download/v0.1.1/lite0.1.1-osx10.15.x-ocv4.5.2-ffmpeg4.2.2-onnxruntime1.9.0.zip) * [x] [lite0.1.1-osx10.15.x-ocv4.5.2-ffmpeg4.2.2-onnxruntime1.10.0.zip](https://github.com/DefTruth/lite.ai.toolkit/releases/download/v0.1.1/lite0.1.1-osx10.15.x-ocv4.5.2-ffmpeg4.2.2-onnxruntime1.10.0.zip) * [x] [lite0.1.1-ubuntu18.04-ocv4.5.2-ffmpeg4.2.2-onnxruntime1.8.1.zip](https://github.com/DefTruth/lite.ai.toolkit/releases/download/v0.1.1/lite0.1.1-ubuntu18.04-ocv4.5.2-ffmpeg4.2.2-onnxruntime1.8.1.zip) * [x] [lite0.1.1-ubuntu18.04-ocv4.5.2-ffmpeg4.2.2-onnxruntime1.9.0.zip](https://github.com/DefTruth/lite.ai.toolkit/releases/download/v0.1.1/lite0.1.1-ubuntu18.04-ocv4.5.2-ffmpeg4.2.2-onnxruntime1.9.0.zip) * [x] [lite0.1.1-ubuntu18.04-ocv4.5.2-ffmpeg4.2.2-onnxruntime1.10.0.zip](https://github.com/DefTruth/lite.ai.toolkit/releases/download/v0.1.1/lite0.1.1-ubuntu18.04-ocv4.5.2-ffmpeg4.2.2-onnxruntime1.10.0.zip) 在Linux下，为了链接到预编译库，使用前，需要先将`lite.ai.toolkit/lib`的路径添加到LD_LIBRARY_PATH. ```shell export LD_LIBRARY_PATH=YOUR-PATH-TO/lite.ai.toolkit/lib:$LD_LIBRARY_PATH export LIBRARY_PATH=YOUR-PATH-TO/lite.ai.toolkit/lib:$LIBRARY_PATH # (may need) ``` ## 快速配置 👀 可以参考以下的CMakeLists.txt，快速配置lite.ai.toolkit。👇👀 ```cmake set(LITE_AI_DIR ${CMAKE_SOURCE_DIR}/lite.ai.toolkit) include_directories(${LITE_AI_DIR}/include) link_directories(${LITE_AI_DIR}/lib}) set(TOOLKIT_LIBS lite.ai.toolkit onnxruntime) set(OpenCV_LIBS opencv_core opencv_imgcodecs opencv_imgproc opencv_video opencv_videoio) add_executable(lite_yolov5 examples/test_lite_yolov5.cpp) target_link_libraries(lite_yolov5 ${TOOLKIT_LIBS} ${OpenCV_LIBS}) ``` ## 目录 📖💡 * [核心特征](#lite.ai.toolkit-Core-Features) * [快速开始](#lite.ai.toolkit-Quick-Start) * [技术规划](#lite.ai.toolkit-RoadMap) * [重要更新](#lite.ai.toolkit-Important-Updates) * [模型支持矩阵](#lite.ai.toolkit-Supported-Models-Matrix) * [编译文档](#lite.ai.toolkit-Build-Lite.AI.ToolKit) * [模型下载](#lite.ai.toolkit-Model-Zoo) * [应用案例](#lite.ai.toolkit-Examples-for-Lite.AI.ToolKit) * [开源协议](#lite.ai.toolkit-License) * [引用参考](#lite.ai.toolkit-References) * [如何添加您的模型](#lite.ai.toolkit-Contribute) ## 1. 快速开始 🌟🌟

#### 案例0: 使用[YOLOv5](https://github.com/ultralytics/yolov5) 进行目标检测。请从Model-Zoo[²](#lite.ai.toolkit-2) 下载模型文件。 ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../hub/onnx/cv/yolov5s.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_yolov5_1.jpg"; std::string save_img_path = "../../../logs/test_lite_yolov5_1.jpg"; auto *yolov5 = new lite::cv::detection::YoloV5(onnx_path); std::vector detected_boxes; cv::Mat img_bgr = cv::imread(test_img_path); yolov5->detect(img_bgr, detected_boxes); lite::utils::draw_boxes_inplace(img_bgr, detected_boxes); cv::imwrite(save_img_path, img_bgr); delete yolov5; } ``` ## 2. 重要更新 🆕

Click here to see details of Important Updates!

| Date | Model | C++ | Paper | Code | Awesome | Type | |:------------:|:------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------:|:-------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------:|:------------:| | 【2022/04/03】 | [MODNet](https://github.com/ZHKKKe/MODNet) | [link](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_modnet.cpp) | [AAAI 2022](https://arxiv.org/pdf/2011.11961.pdf) | [code](https://github.com/ZHKKKe/MODNet) | ![](https://img.shields.io/github/stars/ZHKKKe/MODNet.svg?style=social) | matting | | 【2022/03/23】 | [PIPNtet](https://github.com/jhb86253817/PIPNet) | [link](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_pipnet98.cpp) | [CVPR 2021](https://arxiv.org/abs/2003.03771) | [code](https://github.com/jhb86253817/PIPNet) | ![](https://img.shields.io/github/stars/jhb86253817/PIPNet.svg?style=social) | face::align | | 【2022/01/19】 | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face) | [link](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolo5face.cpp) | [arXiv 2021](https://arxiv.org/abs/2105.12931) | [code](https://github.com/deepcam-cn/yolov5-face) | ![](https://img.shields.io/github/stars/deepcam-cn/yolov5-face.svg?style=social) | face::detect | | 【2022/01/07】 | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd/) | [link](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_scrfd.cpp) | [CVPR 2021](https://arxiv.org/abs/2105.04714) | [code](https://github.com/deepinsight/insightface/blob/master/detection/scrfd/) | ![](https://img.shields.io/github/stars/deepinsight/insightface.svg?style=social) | face::detect | | 【2021/12/27】 | [NanoDetPlus](https://github.com/RangiLyu/nanodet) | [link](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_nanodet_plus.cpp) | [blog](https://zhuanlan.zhihu.com/p/449912627) | [code](https://github.com/RangiLyu/nanodet) | ![](https://img.shields.io/github/stars/RangiLyu/nanodet.svg?style=social) | detection | | 【2021/12/08】 | [MGMatting](https://github.com/yucornetto/MGMatting) | [link](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_mg_matting.cpp) | [CVPR 2021](https://arxiv.org/abs/2012.06722) | [code](https://github.com/yucornetto/MGMatting) | ![](https://img.shields.io/github/stars/yucornetto/MGMatting.svg?style=social) | matting | | 【2021/11/11】 | [YoloV5_V_6_0](https://github.com/ultralytics/yolov5/releases/tag/v6.0) | [link](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolov5_v6.0.cpp) | [doi](https://zenodo.org/record/5563715#.YbXffH1Bzfs) | [code](https://github.com/ultralytics/yolov5/releases/tag/v6.0) | ![](https://img.shields.io/github/stars/ultralytics/yolov5.svg?style=social) | detection | | 【2021/10/26】 | [YoloX_V_0_1_1](https://github.com/Megvii-BaseDetection/YOLOX/releases/tag/0.1.1rc0) | [link](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolox_v0.1.1.cpp) | [arXiv 2021](https://arxiv.org/abs/2107.08430) | [code](https://github.com/Megvii-BaseDetection/YOLOX) | ![](https://img.shields.io/github/stars/Megvii-BaseDetection/YOLOX.svg?style=social) | detection | | 【2021/10/02】 | [NanoDet](https://github.com/RangiLyu/nanodet) | [link](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_nanodet.cpp) | [blog](https://zhuanlan.zhihu.com/p/306530300) | [code](https://github.com/RangiLyu/nanodet) | ![](https://img.shields.io/github/stars/RangiLyu/nanodet.svg?style=social) | detection | | 【2021/09/20】 | [RobustVideoMatting](https://github.com/PeterL1n/RobustVideoMatting) | [link](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_rvm.cpp) | [WACV 2022](https://arxiv.org/abs/2108.11515) | [code](https://github.com/PeterL1n/RobustVideoMatting) | ![](https://img.shields.io/github/stars/PeterL1n/RobustVideoMatting.svg?style=social) | matting | | 【2021/09/02】 | [YOLOP](https://github.com/hustvl/YOLOP) | [link](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolop.cpp) | [arXiv 2021](https://arxiv.org/abs/2108.11250) | [code](https://github.com/hustvl/YOLOP) | ![](https://img.shields.io/github/stars/hustvl/YOLOP.svg?style=social) | detection |

## 3. 模型支持矩阵

* / = 暂不支持. * ✅ = 可以运行，且官方支持. * ✔️ = 可以运行，但非官方支持. * ❔ = 计划中，但不会很快实现，也许几个月后. | Class | Size | Type | Demo | ONNXRuntime | MNN | NCNN | TNN | MacOS | Linux | Windows | Android | |:-----------------------------------------------------------------------------------------------------------------:|:-----:|:----------------:|:----------------------------------------------------------------------------------------------------------------------:|:-----------:|:---:|:----:|:---:|:-----:|:-----:|:-------:|:-------:| | [YoloV5](https://github.com/ultralytics/yolov5) | 28M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolov5.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [YoloV3](https://github.com/onnx/models/blob/master/vision/object_detection_segmentation/yolov3) | 236M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolov3.cpp) | ✅ | / | / | / | ✅ | ✔️ | ✔️ | / | | [TinyYoloV3](https://github.com/onnx/models/blob/master/vision/object_detection_segmentation/tiny-yolov3) | 33M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_tiny_yolov3.cpp) | ✅ | / | / | / | ✅ | ✔️ | ✔️ | / | | [YoloV4](https://github.com/argusswift/YOLOv4-pytorch) | 176M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolov4.cpp) | ✅ | / | / | / | ✅ | ✔️ | ✔️ | / | | [SSD](https://github.com/onnx/models/blob/master/vision/object_detection_segmentation/ssd) | 76M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_ssd.cpp) | ✅ | / | / | / | ✅ | ✔️ | ✔️ | / | | [SSDMobileNetV1](https://github.com/onnx/models/blob/master/vision/object_detection_segmentation/ssd-mobilenetv1) | 27M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_ssd_mobilenetv1.cpp) | ✅ | / | / | / | ✅ | ✔️ | ✔️ | / | | [YoloX](https://github.com/Megvii-BaseDetection/YOLOX) | 3.5M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolox.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [TinyYoloV4VOC](https://github.com/bubbliiiing/yolov4-tiny-pytorch) | 22M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_tiny_yolov4_voc.cpp) | ✅ | / | / | / | ✅ | ✔️ | ✔️ | / | | [TinyYoloV4COCO](https://github.com/bubbliiiing/yolov4-tiny-pytorch) | 22M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_tiny_yolov4_coco.cpp) | ✅ | / | / | / | ✅ | ✔️ | ✔️ | / | | [YoloR](https://github.com/WongKinYiu/yolor) | 39M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolor.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [ScaledYoloV4](https://github.com/WongKinYiu/ScaledYOLOv4) | 270M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_scaled_yolov4.cpp) | ✅ | / | / | / | ✅ | ✔️ | ✔️ | / | | [EfficientDet](https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch) | 15M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_efficientdet.cpp) | ✅ | / | / | / | ✅ | ✔️ | ✔️ | / | | [EfficientDetD7](https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch) | 220M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_efficientdet_d7.cpp) | ✅ | / | / | / | ✅ | ✔️ | ✔️ | / | | [EfficientDetD8](https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch) | 322M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_efficientdet_d8.cpp) | ✅ | / | / | / | ✅ | ✔️ | ✔️ | / | | [YOLOP](https://github.com/hustvl/YOLOP) | 30M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolop.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [NanoDet](https://github.com/RangiLyu/nanodet) | 1.1M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_nanodet.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [NanoDetPlus](https://github.com/RangiLyu/nanodet) | 4.5M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_nanodet_plus.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [NanoDetEffi...](https://github.com/RangiLyu/nanodet) | 12M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_nanodet_efficientnet_lite.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [YoloX_V_0_1_1](https://github.com/Megvii-BaseDetection/YOLOX) | 3.5M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolox_v0.1.1.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [YoloV5_V_6_0](https://github.com/ultralytics/yolov5) | 7.5M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolov5_v6.0.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [GlintArcFace](https://github.com/deepinsight/insightface/tree/master/recognition/arcface_torch) | 92M | *faceid* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_glint_arcface.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [GlintCosFace](https://github.com/deepinsight/insightface/tree/master/recognition/arcface_torch) | 92M | *faceid* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_glint_cosface.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | / | | [GlintPartialFC](https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc) | 170M | *faceid* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_glint_partial_fc.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | / | | [FaceNet](https://github.com/timesler/facenet-pytorch) | 89M | *faceid* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_facenet.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | / | | [FocalArcFace](https://github.com/ZhaoJ9014/face.evoLVe.PyTorch) | 166M | *faceid* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_focal_arcface.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | / | | [FocalAsiaArcFace](https://github.com/ZhaoJ9014/face.evoLVe.PyTorch) | 166M | *faceid* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_focal_asia_arcface.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | / | | [TencentCurricularFace](https://github.com/Tencent/TFace/tree/master/tasks/distfc) | 249M | *faceid* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_tencent_curricular_face.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | / | | [TencentCifpFace](https://github.com/Tencent/TFace/tree/master/tasks/cifp) | 130M | *faceid* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_tencent_cifp_face.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | / | | [CenterLossFace](https://github.com/louis-she/center-loss.pytorch) | 280M | *faceid* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_center_loss_face.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | / | | [SphereFace](https://github.com/clcarwin/sphereface_pytorch) | 80M | *faceid* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_sphere_face.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | / | | [PoseRobustFace](https://github.com/penincillin/DREAM) | 92M | *faceid* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_pose_robust_face.cpp) | ✅ | / | / | / | ✅ | ✔️ | ✔️ | / | | [NaivePoseRobustFace](https://github.com/penincillin/DREAM) | 43M | *faceid* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_naive_pose_robust_face.cpp) | ✅ | / | / | / | ✅ | ✔️ | ✔️ | / | | [MobileFaceNet](https://github.com/Xiaoccer/MobileFaceNet_Pytorch) | 3.8M | *faceid* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_mobile_facenet.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [CavaGhostArcFace](https://github.com/cavalleria/cavaface.pytorch) | 15M | *faceid* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_cava_ghost_arcface.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [CavaCombinedFace](https://github.com/cavalleria/cavaface.pytorch) | 250M | *faceid* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_cava_combined_face.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | / | | [MobileSEFocalFace](https://github.com/grib0ed0v/face_recognition.pytorch) | 4.5M | *faceid* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_mobilese_focal_face.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [RobustVideoMatting](https://github.com/PeterL1n/RobustVideoMatting) | 14M | *matting* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_rvm.cpp) | ✅ | ✅ | / | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [MGMatting](https://github.com/yucornetto/MGMatting) | 113M | *matting* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_mg_matting.cpp) | ✅ | ✅ | / | ✅ | ✅ | ✔️ | ✔️ | / | | [MODNet](https://github.com/ZHKKKe/MODNet) | 24M | *matting* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_modnet.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | / | | [MODNetDyn](https://github.com/ZHKKKe/MODNet) | 24M | *matting* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_modnet_dyn.cpp) | ✅ | / | / | / | ✅ | ✔️ | ✔️ | / | | [BackgroundMattingV2](https://github.com/PeterL1n/BackgroundMattingV2) | 20M | *matting* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_backgroundmattingv2.cpp) | ✅ | ✅ | / | ✅ | ✅ | ✔️ | ✔️ | / | | [BackgroundMattingV2Dyn](https://github.com/PeterL1n/BackgroundMattingV2) | 20M | *matting* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_backgroundmattingv2_dyn.cpp) | ✅ | / | / | / | ✅ | ✔️ | ✔️ | / | | [UltraFace](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB) | 1.1M | *face::detect* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_ultraface.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [RetinaFace](https://github.com/biubug6/Pytorch_Retinaface) | 1.6M | *face::detect* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_retinaface.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [FaceBoxes](https://github.com/zisianw/FaceBoxes.PyTorch) | 3.8M | *face::detect* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_faceboxes.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [FaceBoxesV2](https://github.com/jhb86253817/FaceBoxesV2) | 3.8M | *face::detect* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_faceboxesv2.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [SCRFD](https://github.com/deepinsight/insightface/blob/master/detection/scrfd/) | 2.5M | *face::detect* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_scrfd.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [YOLO5Face](https://github.com/deepcam-cn/yolov5-face) | 4.8M | *face::detect* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolo5face.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [PFLD](https://github.com/Hsintao/pfld_106_face_landmarks) | 1.0M | *face::align* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_pfld.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [PFLD98](https://github.com/polarisZhao/PFLD-pytorch) | 4.8M | *face::align* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_pfld98.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [MobileNetV268](https://github.com/cunjian/pytorch_face_landmark) | 9.4M | *face::align* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_mobilenetv2_68.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [MobileNetV2SE68](https://github.com/cunjian/pytorch_face_landmark) | 11M | *face::align* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_mobilenetv2_se_68.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [PFLD68](https://github.com/cunjian/pytorch_face_landmark) | 2.8M | *face::align* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_pfld68.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [FaceLandmark1000](https://github.com/Single430/FaceLandmark1000) | 2.0M | *face::align* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_face_landmarks_1000.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [PIPNet98](https://github.com/jhb86253817/PIPNet) | 44.0M | *face::align* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_pipnet98.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [PIPNet68](https://github.com/jhb86253817/PIPNet) | 44.0M | *face::align* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_pipnet68.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [PIPNet29](https://github.com/jhb86253817/PIPNet) | 44.0M | *face::align* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_pipnet29.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [PIPNet19](https://github.com/jhb86253817/PIPNet) | 44.0M | *face::align* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_pipnet19.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [FSANet](https://github.com/omasaht/headpose-fsanet-pytorch) | 1.2M | *face::pose* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_fsanet.cpp) | ✅ | ✅ | / | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [AgeGoogleNet](https://github.com/onnx/models/tree/master/vision/body_analysis/age_gender) | 23M | *face::attr* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_age_googlenet.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [GenderGoogleNet](https://github.com/onnx/models/tree/master/vision/body_analysis/age_gender) | 23M | *face::attr* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_gender_googlenet.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [EmotionFerPlus](https://github.com/onnx/models/blob/master/vision/body_analysis/emotion_ferplus) | 33M | *face::attr* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_emotion_ferplus.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [VGG16Age](https://github.com/onnx/models/tree/master/vision/body_analysis/age_gender) | 514M | *face::attr* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_vgg16_age.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | / | | [VGG16Gender](https://github.com/onnx/models/tree/master/vision/body_analysis/age_gender) | 512M | *face::attr* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_vgg16_gender.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | / | | [SSRNet](https://github.com/oukohou/SSR_Net_Pytorch) | 190K | *face::attr* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_ssrnet.cpp) | ✅ | ✅ | / | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [EfficientEmotion7](https://github.com/HSE-asavchenko/face-emotion-recognition) | 15M | *face::attr* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_efficient_emotion7.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [EfficientEmotion8](https://github.com/HSE-asavchenko/face-emotion-recognition) | 15M | *face::attr* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_efficient_emotion8.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [MobileEmotion7](https://github.com/HSE-asavchenko/face-emotion-recognition) | 13M | *face::attr* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_mobile_emotion7.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [ReXNetEmotion7](https://github.com/HSE-asavchenko/face-emotion-recognition) | 30M | *face::attr* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_rexnet_emotion7.cpp) | ✅ | ✅ | / | ✅ | ✅ | ✔️ | ✔️ | / | | [EfficientNetLite4](https://github.com/onnx/models/blob/master/vision/classification/efficientnet-lite4) | 49M | *classification* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_efficientnet_lite4.cpp) | ✅ | ✅ | / | ✅ | ✅ | ✔️ | ✔️ | / | | [ShuffleNetV2](https://github.com/onnx/models/blob/master/vision/classification/shufflenet) | 8.7M | *classification* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_shufflenetv2.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [DenseNet121](https://pytorch.org/hub/pytorch_vision_densenet/) | 30.7M | *classification* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_densenet.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | / | | [GhostNet](https://pytorch.org/hub/pytorch_vision_ghostnet/) | 20M | *classification* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_ghostnet.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [HdrDNet](https://pytorch.org/hub/pytorch_vision_hardnet//) | 13M | *classification* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_hardnet.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [IBNNet](https://pytorch.org/hub/pytorch_vision_ibnnet/) | 97M | *classification* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_ibnnet.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | / | | [MobileNetV2](https://pytorch.org/hub/pytorch_vision_mobilenet_v2/) | 13M | *classification* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_mobilenetv2.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [ResNet](https://pytorch.org/hub/pytorch_vision_resnet/) | 44M | *classification* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_resnet.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | / | | [ResNeXt](https://pytorch.org/hub/pytorch_vision_resnext/) | 95M | *classification* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_resnext.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | / | | [DeepLabV3ResNet101](https://pytorch.org/hub/pytorch_vision_deeplabv3_resnet101/) | 232M | *segmentation* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_deeplabv3_resnet101.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | / | | [FCNResNet101](https://pytorch.org/hub/pytorch_vision_fcn_resnet101/) | 207M | *segmentation* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_fcn_resnet101.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | / | | [FastStyleTransfer](https://github.com/onnx/models/blob/master/vision/style_transfer/fast_neural_style) | 6.4M | *style* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_fast_style_transfer.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [Colorizer](https://github.com/richzhang/colorization) | 123M | *colorization* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_colorizer.cpp) | ✅ | ✅ | / | ✅ | ✅ | ✔️ | ✔️ | / | | [SubPixelCNN](https://github.com/niazwazir/SUB_PIXEL_CNN) | 234K | *resolution* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_subpixel_cnn.cpp) | ✅ | ✅ | / | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [SubPixelCNN](https://github.com/niazwazir/SUB_PIXEL_CNN) | 234K | *resolution* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_subpixel_cnn.cpp) | ✅ | ✅ | / | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [InsectDet](https://github.com/quarrying/quarrying-insect-id) | 27M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_insectdet.cpp) | ✅ | ✅ | / | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [InsectID](https://github.com/quarrying/quarrying-insect-id) | 22M | *classification* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_insectid.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ✔️ | ❔ | | [PlantID](https://github.com/quarrying/quarrying-plant-id) | 30M | *classification* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_plantid.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ✔️ | ❔ | | [YOLOv5BlazeFace](https://github.com/deepcam-cn/yolov5-face) | 3.4M | *face::detect* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolov5_blazeface.cpp) | ✅ | ✅ | / | / | ✅ | ✔️ | ✔️ | ❔ | | [YoloV5_V_6_1](https://github.com/ultralytics/yolov5/releases/tag/v6.1) | 7.5M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolov5_v6.1.cpp) | ✅ | ✅ | / | / | ✅ | ✔️ | ✔️ | ❔ | | [HeadSeg](https://github.com/minivision-ai/photo2cartoon) | 31M | *segmentation* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_head_seg.cpp) | ✅ | ✅ | / | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [FemalePhoto2Cartoon](https://github.com/minivision-ai/photo2cartoon) | 15M | *style* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_female_photo2cartoon.cpp) | ✅ | ✅ | / | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [FastPortraitSeg](https://github.com/YexingWan/Fast-Portrait-Segmentation) | 400k | *segmentation* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_fast_portrait_seg.cpp) | ✅ | ✅ | / | / | ✅ | ✔️ | ✔️ | ❔ | | [PortraitSegSINet](https://github.com/clovaai/ext_portrait_segmentation) | 380k | *segmentation* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_portrait_seg_sinet.cpp) | ✅ | ✅ | / | / | ✅ | ✔️ | ✔️ | ❔ | | [PortraitSegExtremeC3Net](https://github.com/clovaai/ext_portrait_segmentation) | 180k | *segmentation* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_portrait_seg_extremec3net.cpp) | ✅ | ✅ | / | / | ✅ | ✔️ | ✔️ | ❔ | | [FaceHairSeg](https://github.com/kampta/face-seg) | 18M | *segmentation* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_face_hair_seg.cpp) | ✅ | ✅ | / | / | ✅ | ✔️ | ✔️ | ❔ | | [HairSeg](https://github.com/akirasosa/mobile-semantic-segmentation) | 18M | *segmentation* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_hair_seg.cpp) | ✅ | ✅ | / | / | ✅ | ✔️ | ✔️ | ❔ | | [MobileHumanMatting](https://github.com/lizhengwei1992/mobile_phone_human_matting) | 3M | *matting* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_mobile_human_matting.cpp) | ✅ | ✅ | / | / | ✅ | ✔️ | ✔️ | ❔ | | [MobileHairSeg](https://github.com/wonbeomjang/mobile-hair-segmentation-pytorch) | 14M | *segmentation* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_mobile_hair_seg.cpp) | ✅ | ✅ | / | / | ✅ | ✔️ | ✔️ | ❔ | | [YOLOv6](https://github.com/meituan/YOLOv6) | 17M | *detection* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolov6.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [FaceParsingBiSeNet](https://github.com/zllrunning/face-parsing.PyTorch) | 50M | *segmentation* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_face_parsing_bisenet.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [FaceParsingBiSeNetDyn](https://github.com/zllrunning/face-parsing.PyTorch) | 50M | *segmentation* | [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_face_parsing_bisenet_dyn.cpp) | ✅ | / | / | / | / | ✔️ | ✔️ | ❔ | ## 4. 编译文档

* MacOS: 从**Lite.Ai.ToolKit** 源码编译**MacOS**下的动态库。需要注意的是**Lite.Ai.ToolKit** 使用**onnxruntime**作为默认的后端，因为onnxruntime支持大部分onnx的原生算子，具有更高的易用性。如何编译Linux和Windows版本？点击 ▶️ 查看。 ```shell git clone --depth=1 https://github.com/DefTruth/lite.ai.toolkit.git # 最新源码 cd lite.ai.toolkit && sh ./build.sh # 对于MacOS, 你可以直接利用本项目包含的OpenCV, ONNXRuntime, MNN, NCNN and TNN依赖库，无需重新编译 ```

💡️ Linux 和 Windows

### Linux 和 Windows ⚠️ **Lite.Ai.ToolKit** 的发行版本目前不直接支持Linux和Windows，你需要从下载**Lite.Ai.ToolKit**的源码进行构建。首先，你需要下载(如果有官方编译好的发行版本的话)或编译**OpenCV** 、**ONNXRuntime** 和其他你需要的推理引擎，如MNN、NCNN、TNN，然后把它们的头文件分别放入各自对应的文件夹，或者直接使用本项目提供的头文件。本项目的依赖库头文件是直接从相应的官方库拷贝而来的，但不同操作系统下的动态库需要重新编译或下载，MacOS用户可以直接使用本项目提供的各个依赖库的动态库。 * **lite.ai.toolkit/opencv2** ```shell cp -r you-path-to-downloaded-or-built-opencv/include/opencv4/opencv2 lite.ai.toolkit/opencv2 ``` * **lite.ai.toolkit/onnxruntime** ```shell cp -r you-path-to-downloaded-or-built-onnxruntime/include/onnxruntime lite.ai.toolkit/onnxruntime ``` * **lite.ai.toolkit/MNN** ```shell cp -r you-path-to-downloaded-or-built-MNN/include/MNN lite.ai.toolkit/MNN ``` * **lite.ai.toolkit/ncnn** ```shell cp -r you-path-to-downloaded-or-built-ncnn/include/ncnn lite.ai.toolkit/ncnn ``` * **lite.ai.toolkit/tnn** ```shell cp -r you-path-to-downloaded-or-built-TNN/include/tnn lite.ai.toolkit/tnn ``` 然后把各个依赖库拷贝到**lite.ai.toolkit/lib/(linux|windows)** 文件夹。请参考依赖库的编译文档[¹](#lite.ai.toolkit-1)。 * **lite.ai.toolkit/lib/(linux|windows)** ```shell cp you-path-to-downloaded-or-built-opencv/lib/*opencv* lite.ai.toolkit/lib/(linux|windows)/ cp you-path-to-downloaded-or-built-onnxruntime/lib/*onnxruntime* lite.ai.toolkit/lib/(linux|windows)/ cp you-path-to-downloaded-or-built-MNN/lib/*MNN* lite.ai.toolkit/lib/(linux|windows)/ cp you-path-to-downloaded-or-built-ncnn/lib/*ncnn* lite.ai.toolkit/lib/(linux|windows)/ cp you-path-to-downloaded-or-built-TNN/lib/*TNN* lite.ai.toolkit/lib/(linux|windows)/ ``` 注意，你还需要安装ffmpeg(<=4.2.2)，因为opencv的videoio模块依赖ffmpeg进行mp4的编解码。参考[issue#203](https://github.com/DefTruth/lite.ai.toolkit/issues/6) . 在MacOS下，ffmpeg4.2.2已经作为一个自定义依赖库被我打包进lite.ai.toolkit，不需要再从HomeBrew安装为系统库，因此lite.ai.toolkit是单体的，你可以把它移植到app里面，不用心另一台运行app的机器没有ffmpeg，MacOS版本的lite.ai.toolkit已经包含ffmpeg. 在Windows下，opencv官方团队已经提供了用于opencv的ffmpeg预编译库。在Linux下编译opencv时，请确保-DWITH_FFMPEG=ON，并检查是否链接到ffmpeg. * 先编译ffmpeg，注意必须是低版本的，高于4.4的，opencv会不兼容。 ```shell git clone --depth=1 https://git.ffmpeg.org/ffmpeg.git -b n4.2.2 cd ffmpeg ./configure --enable-shared --disable-x86asm --prefix=/usr/local/opt/ffmpeg --disable-static make -j8 make install ``` * 然后，编译带ffmpeg支持的OpenCV，指定-DWITH_FFMPEG=ON ```shell #!/bin/bash mkdir build cd build cmake .. \ -D CMAKE_BUILD_TYPE=Release \ -D CMAKE_INSTALL_PREFIX=your-path-to-custom-dir \ -D BUILD_TESTS=OFF \ -D BUILD_PERF_TESTS=OFF \ -D BUILD_opencv_python3=OFF \ -D BUILD_opencv_python2=OFF \ -D BUILD_SHARED_LIBS=ON \ -D BUILD_opencv_apps=OFF \ -D WITH_FFMPEG=ON make -j8 make install cd .. ``` 编译完opencv后，你就可以按照上述的步骤，继续编译lite.ai.toolkit. * Windows: 你可以参考[issue#6](https://github.com/DefTruth/lite.ai.toolkit/issues/6) ，讨论了常见的编译问题。 * Linux: 参考MacOS下的编译，替换Linux版本的依赖库即可。Linux下的发行版本将会在近期添加 ~ [issue#2](https://github.com/DefTruth/lite.ai.toolkit/issues/2) * 令人开心的消息!!! : 🚀 你可以直接下载最新的**ONNXRuntime**官方构建的动态库，包含Windows, Linux, MacOS and Arm的版本!!! CPU和GPU的版本均可获得。不需要再从源码进行编译了，nice。可以从[v1.8.1](https://github.com/microsoft/onnxruntime/releases) 下载最新的动态库. 我目前在**Lite.Ai.ToolKit**中用的是1.7.0，你可以从[v1.7.0](https://github.com/microsoft/onnxruntime/releases/tag/v1.7.0) 下载, 但1.8.1应该也是可行的。对于**OpenCV**，请尝试从源码构建(Linux) 或者直接从[OpenCV 4.5.3](https://github.com/opencv/opencv/releases) 下载官方编译好的动态库(Windows). 然后把头文件和依赖库放入上述的文件夹中. * Windows GPU 兼容性: 详见[issue#10](https://github.com/DefTruth/lite.ai.toolkit/issues/10). * Linux GPU 兼容性: 详见[issue#97](https://github.com/DefTruth/lite.ai.toolkit/issues/97).

🔑️ 如何链接Lite.Ai.ToolKit动态库?

* 你可参考以下的CMakeLists.txt设置来链接动态库. ```cmake cmake_minimum_required(VERSION 3.17) project(lite.ai.toolkit.demo) set(CMAKE_CXX_STANDARD 11) # setting up lite.ai.toolkit set(LITE_AI_DIR ${CMAKE_SOURCE_DIR}/lite.ai.toolkit) set(LITE_AI_INCLUDE_DIR ${LITE_AI_DIR}/include) set(LITE_AI_LIBRARY_DIR ${LITE_AI_DIR}/lib) include_directories(${LITE_AI_INCLUDE_DIR}) link_directories(${LITE_AI_LIBRARY_DIR}) set(OpenCV_LIBS opencv_highgui opencv_core opencv_imgcodecs opencv_imgproc opencv_video opencv_videoio ) # add your executable set(EXECUTABLE_OUTPUT_PATH ${CMAKE_SOURCE_DIR}/examples/build) add_executable(lite_rvm examples/test_lite_rvm.cpp) target_link_libraries(lite_rvm lite.ai.toolkit onnxruntime MNN # need, if built lite.ai.toolkit with ENABLE_MNN=ON, default OFF ncnn # need, if built lite.ai.toolkit with ENABLE_NCNN=ON, default OFF TNN # need, if built lite.ai.toolkit with ENABLE_TNN=ON, default OFF ${OpenCV_LIBS}) # link lite.ai.toolkit & other libs. ``` ```shell cd ./build/lite.ai.toolkit/lib && otool -L liblite.ai.toolkit.0.0.1.dylib liblite.ai.toolkit.0.0.1.dylib: @rpath/liblite.ai.toolkit.0.0.1.dylib (compatibility version 0.0.1, current version 0.0.1) @rpath/libopencv_highgui.4.5.dylib (compatibility version 4.5.0, current version 4.5.2) @rpath/libonnxruntime.1.7.0.dylib (compatibility version 0.0.0, current version 1.7.0) ... ``` ```shell cd ../ && tree . ├── bin ├── include │ ├── lite │ │ ├── backend.h │ │ ├── config.h │ │ └── lite.h │ └── ort └── lib └── liblite.ai.toolkit.0.0.1.dylib ``` * 运行已经编译好的examples: ```shell cd ./build/lite.ai.toolkit/bin && ls -lh | grep lite -rwxr-xr-x 1 root staff 301K Jun 26 23:10 liblite.ai.toolkit.0.0.1.dylib ... -rwxr-xr-x 1 root staff 196K Jun 26 23:10 lite_yolov4 -rwxr-xr-x 1 root staff 196K Jun 26 23:10 lite_yolov5 ... ``` ```shell ./lite_yolov5 LITEORT_DEBUG LogId: ../../../hub/onnx/cv/yolov5s.onnx =============== Input-Dims ============== ... detected num_anchors: 25200 generate_bboxes num: 66 Default Version Detected Boxes Num: 5 ``` 为了链接`lite.ai.toolkit`动态库，你需要确保`OpenCV` and `onnxruntime`也被正确地链接。你可以在[CMakeLists.txt](https://github.com/DefTruth/RobustVideoMatting-ncnn-mnn-tnn-onnxruntime/blob/main/CMakeLists.txt) 中找到一个简单且完整的，关于如何正确地链接Lite.AI.ToolKit动态库的应用案例。

## 5. 模型下载

**Lite.Ai.ToolKit** 目前包括将近 **[100+](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.onnx.md)** 流行的开源模型以及 **[500+](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.onnx.md)** 文件，大部分文件是我自己转换的。你可以通过**lite::cv::Type::Class** 语法进行调用，如 **[lite::cv::detection::YoloV5](#lite.ai.toolkit-object-detection)**。更多的细节见[Examples for Lite.Ai.ToolKit](#lite.ai.toolkit-Examples-for-Lite.AI.ToolKit)。注意，由于Google Driver(15G)的存储限制，我无法上传所有的模型文件，国内的小伙伴请使用百度云盘。 | File | Baidu Drive | Google Drive | Docker Hub | Hub (Docs) | |:----:|:-------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------:| | ONNX | [Baidu Drive](https://pan.baidu.com/s/1elUGcx7CZkkjEoYhTMwTRQ) code: 8gin | [Google Drive](https://drive.google.com/drive/folders/1p6uBcxGeyS1exc-T61vL8YRhwjYL4iD2?usp=sharing) | [ONNX Docker v0.1.22.01.08 (28G), v0.1.22.02.02 (400M)](https://hub.docker.com/r/qyjdefdocker/lite.ai.toolkit-onnx-hub/tags) | [ONNX Hub](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.onnx.md) | | MNN | [Baidu Drive](https://pan.baidu.com/s/1KyO-bCYUv6qPq2M8BH_Okg) code: 9v63 | ❔ | [MNN Docker v0.1.22.01.08 (11G), v0.1.22.02.02 (213M)](https://hub.docker.com/r/qyjdefdocker/lite.ai.toolkit-mnn-hub/tags) | [MNN Hub](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.mnn.md) | | NCNN | [Baidu Drive](https://pan.baidu.com/s/1hlnqyNsFbMseGFWscgVhgQ) code: sc7f | ❔ | [NCNN Docker v0.1.22.01.08 (9G), v0.1.22.02.02 (197M)](https://hub.docker.com/r/qyjdefdocker/lite.ai.toolkit-ncnn-hub/tags) | [NCNN Hub](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.ncnn.md) | | TNN | [Baidu Drive](https://pan.baidu.com/s/1lvM2YKyUbEc5HKVtqITpcw) code: 6o6k | ❔ | [TNN Docker v0.1.22.01.08 (11G), v0.1.22.02.02 (217M)](https://hub.docker.com/r/qyjdefdocker/lite.ai.toolkit-tnn-hub/tags) | [TNN Hub](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.tnn.md) | ```shell docker pull qyjdefdocker/lite.ai.toolkit-onnx-hub:v0.1.22.01.08 # (28G) docker pull qyjdefdocker/lite.ai.toolkit-mnn-hub:v0.1.22.01.08 # (11G) docker pull qyjdefdocker/lite.ai.toolkit-ncnn-hub:v0.1.22.01.08 # (9G) docker pull qyjdefdocker/lite.ai.toolkit-tnn-hub:v0.1.22.01.08 # (11G) docker pull qyjdefdocker/lite.ai.toolkit-onnx-hub:v0.1.22.02.02 # (400M) + YOLO5Face docker pull qyjdefdocker/lite.ai.toolkit-mnn-hub:v0.1.22.02.02 # (213M) + YOLO5Face docker pull qyjdefdocker/lite.ai.toolkit-ncnn-hub:v0.1.22.02.02 # (197M) + YOLO5Face docker pull qyjdefdocker/lite.ai.toolkit-tnn-hub:v0.1.22.02.02 # (217M) + YOLO5Face ```

❇️ 命名空间和Lite.Ai.ToolKit算法模块的对应关系

### 命名空间和Lite.Ai.ToolKit算法模块的对应关系 | Namespace | Details | |:---------------------------|:----------------------------------------------------------------------------------------------| | *lite::cv::detection* | Object Detection. one-stage and anchor-free detectors, YoloV5, YoloV4, SSD, etc. ✅ | | *lite::cv::classification* | Image Classification. DensNet, ShuffleNet, ResNet, IBNNet, GhostNet, etc. ✅ | | *lite::cv::faceid* | Face Recognition. ArcFace, CosFace, CurricularFace, etc. ❇️ | | *lite::cv::face* | Face Analysis. *detect*, *align*, *pose*, *attr*, etc. ❇️ | | *lite::cv::face::detect* | Face Detection. UltraFace, RetinaFace, FaceBoxes, PyramidBox, etc. ❇️ | | *lite::cv::face::align* | Face Alignment. PFLD(106), FaceLandmark1000(1000 landmarks), PRNet, etc. ❇️ | | *lite::cv::face::align3d* | 3D Face Alignment. FaceMesh(468 3D landmarks), IrisLandmark(71+5 3D landmarks), etc. ❇️ | | *lite::cv::face::pose* | Head Pose Estimation. FSANet, etc. ❇️ | | *lite::cv::face::attr* | Face Attributes. Emotion, Age, Gender. EmotionFerPlus, VGG16Age, etc. ❇️ | | *lite::cv::segmentation* | Object Segmentation. Such as FCN, DeepLabV3, etc. ❇️ ️ | | *lite::cv::style* | Style Transfer. Contains neural style transfer now, such as FastStyleTransfer. ⚠️ | | *lite::cv::matting* | Image Matting. Object and Human matting. ❇️ ️ | | *lite::cv::colorization* | Colorization. Make Gray image become RGB. ⚠️ | | *lite::cv::resolution* | Super Resolution. ⚠️ | ### Lite.Ai.ToolKit的类与权重文件对应关系说明 Lite.AI.ToolKit的类与权重文件对应关系说明，可以在[lite.ai.toolkit.hub.onnx.md](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.onnx.md) 中找到。比如, *lite::cv::detection::YoloV5* 和 *lite::cv::detection::YoloX* 的权重文件为： | Class | Pretrained ONNX Files | Rename or Converted From (Repo) | Size | |:-----------------------------:|:---------------------:|:----------------------------------------------------------------:|:-----:| | *lite::cv::detection::YoloV5* | yolov5l.onnx | [yolov5](https://github.com/ultralytics/yolov5) (🔥🔥💥↑) | 188Mb | | *lite::cv::detection::YoloV5* | yolov5m.onnx | [yolov5](https://github.com/ultralytics/yolov5) (🔥🔥💥↑) | 85Mb | | *lite::cv::detection::YoloV5* | yolov5s.onnx | [yolov5](https://github.com/ultralytics/yolov5) (🔥🔥💥↑) | 29Mb | | *lite::cv::detection::YoloV5* | yolov5x.onnx | [yolov5](https://github.com/ultralytics/yolov5) (🔥🔥💥↑) | 351Mb | | *lite::cv::detection::YoloX* | yolox_x.onnx | [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) (🔥🔥!!↑) | 378Mb | | *lite::cv::detection::YoloX* | yolox_l.onnx | [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) (🔥🔥!!↑) | 207Mb | | *lite::cv::detection::YoloX* | yolox_m.onnx | [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) (🔥🔥!!↑) | 97Mb | | *lite::cv::detection::YoloX* | yolox_s.onnx | [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) (🔥🔥!!↑) | 34Mb | | *lite::cv::detection::YoloX* | yolox_tiny.onnx | [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) (🔥🔥!!↑) | 19Mb | | *lite::cv::detection::YoloX* | yolox_nano.onnx | [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) (🔥🔥!!↑) | 3.5Mb | 这意味着，你可以通过Lite.Ai.ToolKit中的同一个类，根据你的使用情况，加载任意一个`yolov5*.onnx`或`yolox_*.onnx`，如 *YoloV5*, *YoloX*等. ```c++ auto *yolov5 = new lite::cv::detection::YoloV5("yolov5x.onnx"); // for server auto *yolov5 = new lite::cv::detection::YoloV5("yolov5l.onnx"); auto *yolov5 = new lite::cv::detection::YoloV5("yolov5m.onnx"); auto *yolov5 = new lite::cv::detection::YoloV5("yolov5s.onnx"); // for mobile device auto *yolox = new lite::cv::detection::YoloX("yolox_x.onnx"); auto *yolox = new lite::cv::detection::YoloX("yolox_l.onnx"); auto *yolox = new lite::cv::detection::YoloX("yolox_m.onnx"); auto *yolox = new lite::cv::detection::YoloX("yolox_s.onnx"); auto *yolox = new lite::cv::detection::YoloX("yolox_tiny.onnx"); auto *yolox = new lite::cv::detection::YoloX("yolox_nano.onnx"); // 3.5Mb only ! ```

🔑️ 如何从通过Docker Hub下载Model Zoo？

* Firstly, pull the image from docker hub. ```shell docker pull qyjdefdocker/lite.ai.toolkit-mnn-hub:v0.1.22.01.08 # (11G) docker pull qyjdefdocker/lite.ai.toolkit-ncnn-hub:v0.1.22.01.08 # (9G) docker pull qyjdefdocker/lite.ai.toolkit-tnn-hub:v0.1.22.01.08 # (11G) docker pull qyjdefdocker/lite.ai.toolkit-onnx-hub:v0.1.22.01.08 # (28G) ``` * Secondly, run the container with local `share` dir using `docker run -idt xxx`. A minimum example will show you as follows. * make a `share` dir in your local device. ```shell mkdir share # any name is ok. ``` * write `run_mnn_docker_hub.sh` script like: ```shell #!/bin/bash PORT1=6072 PORT2=6084 SERVICE_DIR=/Users/xxx/Desktop/your-path-to/share CONRAINER_DIR=/home/hub/share CONRAINER_NAME=mnn_docker_hub_d docker run -idt -p ${PORT2}:${PORT1} -v ${SERVICE_DIR}:${CONRAINER_DIR} --shm-size=16gb --name ${CONRAINER_NAME} qyjdefdocker/lite.ai.toolkit-mnn-hub:v0.1.22.01.08 ``` * Finally, copy the model weights from `/home/hub/mnn/cv` to your local `share` dir. ```shell # activate mnn docker. sh ./run_mnn_docker_hub.sh docker exec -it mnn_docker_hub_d /bin/bash # copy the models to the share dir. cd /home/hub cp -rf mnn/cv share/ ```

### Model Hubs lite.ai.toolkit提供大量的预训练模型的ONNX文件. 同时, 更多的模型权重文件详见 [Model Zoo](#lite.ai.toolkit-Model-Zoo) and [ONNX Hub](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.onnx.md), [MNN Hub](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.mnn.md), [TNN Hub](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.tnn.md), [NCNN Hub](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.ncnn.md). ## 6. 应用案例

更多的应用案例详见[examples](https://github.com/DefTruth/lite.ai.toolkit/tree/main/examples/lite/cv) 。点击 ▶️ 可以看到该主题下更多的案例。

或者你可以使用最新的 🔥🔥 ! YOLO 系列检测器[YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) 或 [YoloR](https://github.com/WongKinYiu/yolor) ，它们会获得接近的结果。更多可用的通用目标检测器（80类、COCO）: ```c++ 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 !!! ``` ****

#### 案例1: 使用[RobustVideoMatting2021🔥🔥🔥](https://github.com/PeterL1n/RobustVideoMatting) 进行视频抠图。请从Model-Zoo[²](#lite.ai.toolkit-2) 下载模型文件。 ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../hub/onnx/cv/rvm_mobilenetv3_fp32.onnx"; std::string video_path = "../../../examples/lite/resources/test_lite_rvm_0.mp4"; std::string output_path = "../../../logs/test_lite_rvm_0.mp4"; std::string background_path = "../../../examples/lite/resources/test_lite_matting_bgr.jpg"; auto *rvm = new lite::cv::matting::RobustVideoMatting(onnx_path, 16); // 16 threads std::vector 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; } ``` 输出的结果是:

更多可用的抠图模型（图片抠图、视频抠图、trimap/mask-free、trimap/mask-based）: ```c++ auto *matting = new lite::cv::matting::RobustVideoMatting:(onnx_path); // WACV 2022. auto *matting = new lite::cv::matting::MGMatting(onnx_path); // CVPR 2021 auto *matting = new lite::cv::matting::MODNet(onnx_path); // AAAI 2022 auto *matting = new lite::cv::matting::MODNetDyn(onnx_path); // AAAI 2022 Dynamic Shape Inference. auto *matting = new lite::cv::matting::BackgroundMattingV2(onnx_path); // CVPR 2020 auto *matting = new lite::cv::matting::BackgroundMattingV2Dyn(onnx_path); // CVPR 2020 Dynamic Shape Inference. auto *matting = new lite::cv::matting::MobileHumanMatting(onnx_path); // 3Mb only !!! ``` ****

#### 案例2: 使用[FaceLandmarks1000](https://github.com/Single430/FaceLandmark1000) 进行人脸1000关键点检测。请从Model-Zoo[²](#lite.ai.toolkit-2) 下载模型文件。 ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../hub/onnx/cv/FaceLandmark1000.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_face_landmarks_0.png"; std::string save_img_path = "../../../logs/test_lite_face_landmarks_1000.jpg"; auto *face_landmarks_1000 = new lite::cv::face::align::FaceLandmark1000(onnx_path); lite::types::Landmarks landmarks; cv::Mat img_bgr = cv::imread(test_img_path); face_landmarks_1000->detect(img_bgr, landmarks); lite::utils::draw_landmarks_inplace(img_bgr, landmarks); cv::imwrite(save_img_path, img_bgr); delete face_landmarks_1000; } ``` 输出的结果是:

更多可用的人脸关键点检测器（68点、98点、106点、1000点）: ```c++ auto *align = new lite::cv::face::align::PFLD(onnx_path); // 106 landmarks, 1.0Mb only! auto *align = new lite::cv::face::align::PFLD98(onnx_path); // 98 landmarks, 4.8Mb only! auto *align = new lite::cv::face::align::PFLD68(onnx_path); // 68 landmarks, 2.8Mb only! auto *align = new lite::cv::face::align::MobileNetV268(onnx_path); // 68 landmarks, 9.4Mb only! auto *align = new lite::cv::face::align::MobileNetV2SE68(onnx_path); // 68 landmarks, 11Mb only! auto *align = new lite::cv::face::align::FaceLandmark1000(onnx_path); // 1000 landmarks, 2.0Mb only! auto *align = new lite::cv::face::align::PIPNet98(onnx_path); // 98 landmarks, CVPR2021! auto *align = new lite::cv::face::align::PIPNet68(onnx_path); // 68 landmarks, CVPR2021! auto *align = new lite::cv::face::align::PIPNet29(onnx_path); // 29 landmarks, CVPR2021! auto *align = new lite::cv::face::align::PIPNet19(onnx_path); // 19 landmarks, CVPR2021! ``` ****

#### 案例3: 使用[colorization](https://github.com/richzhang/colorization) 进行图像着色。请从Model-Zoo[²](#lite.ai.toolkit-2) 下载模型文件。 ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../hub/onnx/cv/eccv16-colorizer.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_colorizer_1.jpg"; std::string save_img_path = "../../../logs/test_lite_eccv16_colorizer_1.jpg"; auto *colorizer = new lite::cv::colorization::Colorizer(onnx_path); cv::Mat img_bgr = cv::imread(test_img_path); lite::types::ColorizeContent colorize_content; colorizer->detect(img_bgr, colorize_content); if (colorize_content.flag) cv::imwrite(save_img_path, colorize_content.mat); delete colorizer; } ``` 输出的结果是:

更多可用的着色器模型（灰度图转彩色图）: ```c++ auto *colorizer = new lite::cv::colorization::Colorizer(onnx_path); ``` ****

#### 案例4: 使用[ArcFace](https://github.com/deepinsight/insightface/tree/master/recognition/arcface_torch) 进行人脸识别。请从Model-Zoo[²](#lite.ai.toolkit-2) 下载模型文件。 ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../hub/onnx/cv/ms1mv3_arcface_r100.onnx"; std::string test_img_path0 = "../../../examples/lite/resources/test_lite_faceid_0.png"; std::string test_img_path1 = "../../../examples/lite/resources/test_lite_faceid_1.png"; std::string test_img_path2 = "../../../examples/lite/resources/test_lite_faceid_2.png"; auto *glint_arcface = new lite::cv::faceid::GlintArcFace(onnx_path); lite::types::FaceContent face_content0, face_content1, face_content2; cv::Mat img_bgr0 = cv::imread(test_img_path0); cv::Mat img_bgr1 = cv::imread(test_img_path1); cv::Mat img_bgr2 = cv::imread(test_img_path2); glint_arcface->detect(img_bgr0, face_content0); glint_arcface->detect(img_bgr1, face_content1); glint_arcface->detect(img_bgr2, face_content2); if (face_content0.flag && face_content1.flag && face_content2.flag) { float sim01 = lite::utils::math::cosine_similarity( face_content0.embedding, face_content1.embedding); float sim02 = lite::utils::math::cosine_similarity( face_content0.embedding, face_content2.embedding); std::cout << "Detected Sim01: " << sim << " Sim02: " << sim02 << std::endl; } delete glint_arcface; } ``` 输出的结果是:

> Detected Sim01: 0.721159 Sim02: -0.0626267 更多可用的人脸识别模型（人脸特征提取）: ```c++ auto *recognition = new lite::cv::faceid::GlintCosFace(onnx_path); // DeepGlint(insightface) auto *recognition = new lite::cv::faceid::GlintArcFace(onnx_path); // DeepGlint(insightface) auto *recognition = new lite::cv::faceid::GlintPartialFC(onnx_path); // DeepGlint(insightface) auto *recognition = new lite::cv::faceid::FaceNet(onnx_path); auto *recognition = new lite::cv::faceid::FocalArcFace(onnx_path); auto *recognition = new lite::cv::faceid::FocalAsiaArcFace(onnx_path); auto *recognition = new lite::cv::faceid::TencentCurricularFace(onnx_path); // Tencent(TFace) auto *recognition = new lite::cv::faceid::TencentCifpFace(onnx_path); // Tencent(TFace) auto *recognition = new lite::cv::faceid::CenterLossFace(onnx_path); auto *recognition = new lite::cv::faceid::SphereFace(onnx_path); auto *recognition = new lite::cv::faceid::PoseRobustFace(onnx_path); auto *recognition = new lite::cv::faceid::NaivePoseRobustFace(onnx_path); auto *recognition = new lite::cv::faceid::MobileFaceNet(onnx_path); // 3.8Mb only ! auto *recognition = new lite::cv::faceid::CavaGhostArcFace(onnx_path); auto *recognition = new lite::cv::faceid::CavaCombinedFace(onnx_path); auto *recognition = new lite::cv::faceid::MobileSEFocalFace(onnx_path); // 4.5Mb only ! ``` ****

#### 案例5: 使用[SCRFD 2021](https://github.com/deepinsight/insightface/blob/master/detection/scrfd/) 进行人脸检测。请从Model-Zoo[²](#lite.ai.toolkit-2) 下载模型文件。 ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../hub/onnx/cv/scrfd_2.5g_bnkps_shape640x640.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_face_detector.jpg"; std::string save_img_path = "../../../logs/test_lite_scrfd.jpg"; auto *scrfd = new lite::cv::face::detect::SCRFD(onnx_path); std::vector 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; } ``` 输出的结果是:

更多可用的人脸检测器（轻量级人脸检测器）: ```c++ auto *detector = new lite::face::detect::UltraFace(onnx_path); // 1.1Mb only ! auto *detector = new lite::face::detect::FaceBoxes(onnx_path); // 3.8Mb only ! auto *detector = new lite::face::detect::FaceBoxesv2(onnx_path); // 4.0Mb only ! auto *detector = new lite::face::detect::RetinaFace(onnx_path); // 1.6Mb only ! CVPR2020 auto *detector = new lite::face::detect::SCRFD(onnx_path); // 2.5Mb only ! CVPR2021, Super fast and accurate!! auto *detector = new lite::face::detect::YOLO5Face(onnx_path); // 2021, Super fast and accurate!! auto *detector = new lite::face::detect::YOLOv5BlazeFace(onnx_path); // 2021, Super fast and accurate!! ``` ****

#### 案例6: 使用 [DeepLabV3ResNet101](https://pytorch.org/hub/pytorch_vision_deeplabv3_resnet101/) 进行语义分割. 请从Model-Zoo[²](#lite.ai.toolkit-2) 下载模型文件。 ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../hub/onnx/cv/deeplabv3_resnet101_coco.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_deeplabv3_resnet101.png"; std::string save_img_path = "../../../logs/test_lite_deeplabv3_resnet101.jpg"; auto *deeplabv3_resnet101 = new lite::cv::segmentation::DeepLabV3ResNet101(onnx_path, 16); // 16 threads lite::types::SegmentContent content; cv::Mat img_bgr = cv::imread(test_img_path); deeplabv3_resnet101->detect(img_bgr, content); if (content.flag) { cv::Mat out_img; cv::addWeighted(img_bgr, 0.2, content.color_mat, 0.8, 0., out_img); cv::imwrite(save_img_path, out_img); if (!content.names_map.empty()) { for (auto it = content.names_map.begin(); it != content.names_map.end(); ++it) { std::cout << it->first << " Name: " << it->second << std::endl; } } } delete deeplabv3_resnet101; } ``` 输出的结果是:

更多可用的语义分割模型（人像分割、实例分割）: ```c++ auto *segment = new lite::cv::segmentation::FCNResNet101(onnx_path); auto *segment = new lite::cv::segmentation::DeepLabV3ResNet101(onnx_path); ``` ****

#### 案例7: 使用 [SSRNet](https://github.com/oukohou/SSR_Net_Pytorch) 进行年龄估计. 请从Model-Zoo[²](#lite.ai.toolkit-2) 下载模型文件。 ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../hub/onnx/cv/ssrnet.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_ssrnet.jpg"; std::string save_img_path = "../../../logs/test_lite_ssrnet.jpg"; 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; } ``` 输出的结果是:

更多可用的人脸属性识别模型（性别、年龄、情绪）: ```c++ auto *attribute = new lite::cv::face::attr::AgeGoogleNet(onnx_path); auto *attribute = new lite::cv::face::attr::GenderGoogleNet(onnx_path); auto *attribute = new lite::cv::face::attr::EmotionFerPlus(onnx_path); auto *attribute = new lite::cv::face::attr::VGG16Age(onnx_path); auto *attribute = new lite::cv::face::attr::VGG16Gender(onnx_path); auto *attribute = new lite::cv::face::attr::EfficientEmotion7(onnx_path); // 7 emotions, 15Mb only! auto *attribute = new lite::cv::face::attr::EfficientEmotion8(onnx_path); // 8 emotions, 15Mb only! auto *attribute = new lite::cv::face::attr::MobileEmotion7(onnx_path); // 7 emotions, 13Mb only! auto *attribute = new lite::cv::face::attr::ReXNetEmotion7(onnx_path); // 7 emotions auto *attribute = new lite::cv::face::attr::SSRNet(onnx_path); // age estimation, 190kb only!!! ``` ****

#### 案例8: 使用 [DenseNet](https://pytorch.org/hub/pytorch_vision_densenet/) 进行图片1000分类. 请从Model-Zoo[²](#lite.ai.toolkit-2) 下载模型文件。 ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../hub/onnx/cv/densenet121.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_densenet.jpg"; auto *densenet = new lite::cv::classification::DenseNet(onnx_path); lite::types::ImageNetContent content; cv::Mat img_bgr = cv::imread(test_img_path); densenet->detect(img_bgr, content); if (content.flag) { const unsigned int top_k = content.scores.size(); if (top_k > 0) { for (unsigned int i = 0; i < top_k; ++i) std::cout << i + 1 << ": " << content.labels.at(i) << ": " << content.texts.at(i) << ": " << content.scores.at(i) << std::endl; } } delete densenet; } ``` 输出的结果是:

更多可用的图像分类模型（1000类）: ```c++ auto *classifier = new lite::cv::classification::EfficientNetLite4(onnx_path); auto *classifier = new lite::cv::classification::ShuffleNetV2(onnx_path); // 8.7Mb only! auto *classifier = new lite::cv::classification::GhostNet(onnx_path); auto *classifier = new lite::cv::classification::HdrDNet(onnx_path); auto *classifier = new lite::cv::classification::IBNNet(onnx_path); auto *classifier = new lite::cv::classification::MobileNetV2(onnx_path); // 13Mb only! auto *classifier = new lite::cv::classification::ResNet(onnx_path); auto *classifier = new lite::cv::classification::ResNeXt(onnx_path); ``` ****

#### 案例9: 使用 [FSANet](https://github.com/omasaht/headpose-fsanet-pytorch) 进行头部姿态识别. 请从Model-Zoo[²](#lite.ai.toolkit-2) 下载模型文件。 ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../hub/onnx/cv/fsanet-var.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_fsanet.jpg"; std::string save_img_path = "../../../logs/test_lite_fsanet.jpg"; auto *fsanet = new lite::cv::face::pose::FSANet(onnx_path); cv::Mat img_bgr = cv::imread(test_img_path); lite::types::EulerAngles euler_angles; fsanet->detect(img_bgr, euler_angles); if (euler_angles.flag) { lite::utils::draw_axis_inplace(img_bgr, euler_angles); cv::imwrite(save_img_path, img_bgr); std::cout << "yaw:" << euler_angles.yaw << " pitch:" << euler_angles.pitch << " row:" << euler_angles.roll << std::endl; } delete fsanet; } ``` 输出的结果是:

更多可用的头部姿态识别模型（欧拉角、yaw、pitch、roll）: ```c++ auto *pose = new lite::cv::face::pose::FSANet(onnx_path); // 1.2Mb only! ``` ****

#### 案例10: 使用 [FastStyleTransfer](https://github.com/onnx/models/tree/master/vision/style_transfer/fast_neural_style) 进行风格迁移. 请从Model-Zoo[²](#lite.ai.toolkit-2) 下载模型文件。 ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../hub/onnx/cv/style-candy-8.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_fast_style_transfer.jpg"; std::string save_img_path = "../../../logs/test_lite_fast_style_transfer_candy.jpg"; auto *fast_style_transfer = new lite::cv::style::FastStyleTransfer(onnx_path); lite::types::StyleContent style_content; cv::Mat img_bgr = cv::imread(test_img_path); fast_style_transfer->detect(img_bgr, style_content); if (style_content.flag) cv::imwrite(save_img_path, style_content.mat); delete fast_style_transfer; } ``` 输出的结果是:

更多可用的风格迁移模型（自然风格迁移、其他）: ```c++ auto *transfer = new lite::cv::style::FastStyleTransfer(onnx_path); // 6.4Mb only ``` **** #### 案例11: 使用 [HeadSeg](https://github.com/minivision-ai/photo2cartoon) 进行人像头部分割. 请从Model-Zoo[²](#lite.ai.toolkit-2) 下载模型文件。 ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../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 = "../../../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; } ``` 输出的结果是:

更多可用的人像分割模型（头部分割、肖像分割、头发分割） ```c++ auto *segment = new lite::cv::segmentation::HeadSeg(onnx_path); // 31Mb auto *segment = new lite::cv::segmentation::FastPortraitSeg(onnx_path); // <= 400Kb !!! auto *segment = new lite::cv::segmentation::PortraitSegSINet(onnx_path); // <= 380Kb !!! auto *segment = new lite::cv::segmentation::PortraitSegExtremeC3Net(onnx_path); // <= 180Kb !!! Extreme Tiny !!! auto *segment = new lite::cv::segmentation::FaceHairSeg(onnx_path); // 18M auto *segment = new lite::cv::segmentation::HairSeg(onnx_path); // 18M auto *segment = new lite::cv::segmentation::MobileHairSeg(onnx_path); // 14M ``` **** #### Example12: 使用 [Photo2Cartoon](https://github.com/minivision-ai/photo2cartoon) 进行人像卡通风格化。请从Model-Zoo[²](#lite.ai.toolkit-2) 下载模型文件。 ```c++ #include "lite/lite.h" static void test_default() { std::string head_seg_onnx_path = "../../../hub/onnx/cv/minivision_head_seg.onnx"; std::string cartoon_onnx_path = "../../../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 = "../../../logs/test_lite_female_photo2cartoon_seg.jpg"; std::string save_cartoon_path = "../../../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; } ``` 输出的结果是:

更多的人像风格化模型： ```c++ auto *transfer = new lite::cv::style::FemalePhoto2Cartoon(onnx_path); ``` **** #### Example13: 使用 [FaceParsing](https://github.com/zllrunning/face-parsing.PyTorch) 进行五官分割. 请从Model-Zoo[²](#lite.ai.toolkit-2) 下载模型文件。 ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../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 = "../../../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; } ``` 输出的结果是:

更多的进行五官分割的模型 (hair, eyes, nose, mouth, others)： ```c++ auto *segment = new lite::cv::segmentation::FaceParsingBiSeNet(onnx_path); // 50Mb auto *segment = new lite::cv::segmentation::FaceParsingBiSeNetDyn(onnx_path); // Dynamic Shape Inference. ``` ## 7. 开源协议

[Lite.Ai.ToolKit](#lite.ai.toolkit-Introduction) 的代码采用GPL-3.0协议。 ## 8. 引用参考

本项目参考了以下开源项目。 * [RobustVideoMatting](https://github.com/PeterL1n/RobustVideoMatting) (🔥🔥🔥new!!↑) * [nanodet](https://github.com/RangiLyu/nanodet) (🔥🔥🔥↑) * [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) (🔥🔥🔥new!!↑) * [YOLOP](https://github.com/hustvl/YOLOP) (🔥🔥new!!↑) * [YOLOR](https://github.com/WongKinYiu/yolor) (🔥🔥new!!↑) * [ScaledYOLOv4](https://github.com/WongKinYiu/ScaledYOLOv4) (🔥🔥🔥↑) * [insightface](https://github.com/deepinsight/insightface) (🔥🔥🔥↑) * [yolov5](https://github.com/ultralytics/yolov5) (🔥🔥💥↑) * [TFace](https://github.com/Tencent/TFace) (🔥🔥↑) * [YOLOv4-pytorch](https://github.com/argusswift/YOLOv4-pytorch) (🔥🔥🔥↑) * [Ultra-Light-Fast-Generic-Face-Detector-1MB](https://github.com/Linzaer/Ultra-Light-Fast-Generic-Face-Detector-1MB) (🔥🔥🔥↑)

展开更多引用参考

* [headpose-fsanet-pytorch](https://github.com/omasaht/headpose-fsanet-pytorch) (🔥↑) * [pfld_106_face_landmarks](https://github.com/Hsintao/pfld_106_face_landmarks) (🔥🔥↑) * [onnx-models](https://github.com/onnx/models) (🔥🔥🔥↑) * [SSR_Net_Pytorch](https://github.com/oukohou/SSR_Net_Pytorch) (🔥↑) * [colorization](https://github.com/richzhang/colorization) (🔥🔥🔥↑) * [SUB_PIXEL_CNN](https://github.com/niazwazir/SUB_PIXEL_CNN) (🔥↑) * [torchvision](https://github.com/pytorch/vision) (🔥🔥🔥↑) * [facenet-pytorch](https://github.com/timesler/facenet-pytorch) (🔥↑) * [face.evoLVe.PyTorch](https://github.com/ZhaoJ9014/face.evoLVe.PyTorch) (🔥🔥🔥↑) * [center-loss.pytorch](https://github.com/louis-she/center-loss.pytorch) (🔥🔥↑) * [sphereface_pytorch](https://github.com/clcarwin/sphereface_pytorch) (🔥🔥↑) * [DREAM](https://github.com/penincillin/DREAM) (🔥🔥↑) * [MobileFaceNet_Pytorch](https://github.com/Xiaoccer/MobileFaceNet_Pytorch) (🔥🔥↑) * [cavaface.pytorch](https://github.com/cavalleria/cavaface.pytorch) (🔥🔥↑) * [CurricularFace](https://github.com/HuangYG123/CurricularFace) (🔥🔥↑) * [face-emotion-recognition](https://github.com/HSE-asavchenko/face-emotion-recognition) (🔥↑) * [face_recognition.pytorch](https://github.com/grib0ed0v/face_recognition.pytorch) (🔥🔥↑) * [PFLD-pytorch](https://github.com/polarisZhao/PFLD-pytorch) (🔥🔥↑) * [pytorch_face_landmark](https://github.com/cunjian/pytorch_face_landmark) (🔥🔥↑) * [FaceLandmark1000](https://github.com/Single430/FaceLandmark1000) (🔥🔥↑) * [Pytorch_Retinaface](https://github.com/biubug6/Pytorch_Retinaface) (🔥🔥🔥↑) * [FaceBoxes](https://github.com/zisianw/FaceBoxes.PyTorch) (🔥🔥↑)

## 9. 编译选项未来会增加一些模型的[MNN](https://github.com/alibaba/MNN) 、[NCNN](https://github.com/Tencent/ncnn) 和 [TNN](https://github.com/Tencent/TNN) 支持，但由于算子兼容等原因，也无法确保所有被[ONNXRuntime C++](https://github.com/microsoft/onnxruntime) 支持的模型能够在[MNN](https://github.com/alibaba/MNN) 、[NCNN](https://github.com/Tencent/ncnn) 和 [TNN](https://github.com/Tencent/TNN) 下跑通。所以，如果您想使用本项目支持的所有模型，并且不在意*1~2ms*的性能差距的话，请使用ONNXRuntime版本的实现。[ONNXRuntime](https://github.com/microsoft/onnxruntime) 是本仓库默认的推理引擎。但是如果你确实希望编译支持[MNN](https://github.com/alibaba/MNN) 、[NCNN](https://github.com/Tencent/ncnn) 和 [TNN](https://github.com/Tencent/TNN) 支持的Lite.Ai.ToolKit动态库，你可以按照以下的步骤进行设置。 * 在`build.sh`中添加`DENABLE_MNN=ON` 、`DENABLE_NCNN=ON` 或 `DENABLE_TNN=ON`，比如 ```shell cd build && cmake \ -DCMAKE_BUILD_TYPE=MinSizeRel \ -DINCLUDE_OPENCV=ON \ # 是否打包OpenCV进lite.ai.toolkit，默认ON；否则，你需要单独设置OpenCV -DENABLE_MNN=ON \ # 是否编译MNN版本的模型，默认OFF，目前只支持部分模型 -DENABLE_NCNN=OFF \ # 是否编译NCNN版本的模型，默认OFF，目前只支持部分模型 -DENABLE_TNN=OFF \ # 是否编译TNN版本的模型，默认OFF，目前只支持部分模型 .. && make -j8 ``` * 使用MNN、NCNN或TNN版本的接口，详见案例[demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_nanodet.cpp) ，比如 ```C++ auto *nanodet = new lite::mnn::cv::detection::NanoDet(mnn_path); auto *nanodet = new lite::tnn::cv::detection::NanoDet(proto_path, model_path); auto *nanodet = new lite::ncnn::cv::detection::NanoDet(param_path, bin_path); ``` ## 10. 如何添加您的模型

如何添加您自己的模型以及成为贡献者？具体步骤请参考 [CONTRIBUTING.zh.md](https://github.com/DefTruth/lite.ai.toolkit/issues/191) . ## 11. 感谢 !! 🤗🤗 非常感谢以下的贡献者: * [Windows10 VS2019 CUDA 11.1 Build Docs](https://github.com/DefTruth/lite.ai.toolkit/issues/207) ([@zhanghongyong123456](https://github.com/zhanghongyong123456)) * [Linux Build Docs](https://github.com/DefTruth/lite.ai.toolkit/blob/main/docs/build/Linux.zh.md) ([@lee1221ee](https://github.com/lee1221ee)) * [Some Windows10 Bugs Fixed](https://github.com/DefTruth/lite.ai.toolkit/pull/105) ([@ysc3839](https://github.com/ysc3839), [@AvenSun](https://github.com/AvenSun))