lite.ai.toolkit
<div id="lite.ai.toolkit-Introduction"></div> <!--   -->  <div align='center'> <img src=https://img.shields.io/badge/Linux-pass-brightgreen.svg > <img src=https://img.shields.io/badge/Device-GPU/CPU-yellow.svg > <img src=https://img.shields.io/badge/ONNXRuntime-1.17.1-turquoise.svg > <img src=https://img.shields.io/badge/MNN-2.8.2-hotpink.svg > <img src=https://img.shields.io/badge/TensorRT-10-turquoise.svg > <img src=https://img.shields.io/github/stars/xlite-dev/lite.ai.toolkit.svg?style=social > </div> 🛠**Lite.Ai.ToolKit**: A lite C++ toolkit of 100+ Awesome AI models, such as [Object Detection](#lite.ai.toolkit-object-detection), [Face Detection](#lite.ai.toolkit-face-detection), [Face Recognition](#lite.ai.toolkit-face-recognition), [Segmentation](#lite.ai.toolkit-segmentation), [Matting](#lite.ai.toolkit-matting), etc. See [Model Zoo](#lite.ai.toolkit-Model-Zoo) and [ONNX Hub](https://github.com/xlite-dev/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.onnx.md), [MNN Hub](https://github.com/xlite-dev/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.mnn.md), [TNN Hub](https://github.com/xlite-dev/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.tnn.md), [NCNN Hub](https://github.com/xlite-dev/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.ncnn.md). Welcome to 🌟👆🏻star this repo to support me, many thanks ~ 🎉🎉 <div align='center'> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/5b28aed1-e207-4256-b3ea-3b52f9e68aed' style="height:80px;width:80px;object-fit:cover;"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/28274741-8745-4665-abff-3a384b75f7fa' style="height:80px;width:80px;object-fit:cover;"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/c802858c-6899-4246-8839-5721c43faffe' style="height:80px;width:80px;object-fit:cover;"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/20a18d56-297c-4c72-8153-76d4380fc9ec' style="height:80px;width:80px;object-fit:cover;"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/f4dd5263-8514-4bb0-a0dd-dbe532481aff' style="height:80px;width:80px;object-fit:cover;"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/b6a431d2-225b-416b-8a1e-cf9617d79a63' style="height:80px;width:80px;object-fit:cover;"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/84d3ed6a-b711-4c0a-8e92-a2da05a0d04e' style="height:80px;width:80px;object-fit:cover;"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/157b9e11-fc92-445b-ae0d-0d859c8663ee' style="height:80px;width:80px;object-fit:cover;"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/ef0eeabe-6dbe-4837-9aad-b806a8398697' style="height:80px;width:80px;object-fit:cover;"> </div> ## 📖 News 🔥🔥 <div id="news"></div> - [2026/03] Cache-DiT **[🎉v1.3.0](https://github.com/vipshop/cache-dit)** release is ready, the major updates including: [Ring](https://cache-dit.readthedocs.io/en/latest/user_guide/CONTEXT_PARALLEL) Attention w/ [batched P2P](https://cache-dit.readthedocs.io/en/latest/user_guide/CONTEXT_PARALLEL), [USP](https://cache-dit.readthedocs.io/en/latest/user_guide/CONTEXT_PARALLEL/) (Hybrid Ring and Ulysses), Hybrid 2D and 3D Parallelism (💥[USP + TP](https://cache-dit.readthedocs.io/en/latest/user_guide/HYBRID_PARALLEL/)), VAE-P Comm overhead reduce.  - Most of my time now is focused on **LLM/VLM** Inference. Please check 📖[Awesome-LLM-Inference](https://github.com/xlite-dev/Awesome-LLM-Inference)  and 📖[LeetCUDA](https://github.com/xlite-dev/LeetCUDA)  for more details. Now, [lite.ai.toolkit](https://github.com/xlite-dev/lite.ai.toolkit)  is mainly maintained by 🎉[@wangzijian1010](https://github.com/wangzijian1010). ## Citations 🎉🎉 ```BibTeX @misc{lite.ai.toolkit@2021, title={lite.ai.toolkit: A lite C++ toolkit of 100+ Awesome AI models.}, url={https://github.com/xlite-dev/lite.ai.toolkit}, note={Open-source software available at https://github.com/xlite-dev/lite.ai.toolkit}, author={xlite-dev, wangzijian1010 etc}, year={2021} } ``` ## Features 👏👋 * **Simply and User friendly.** Simply and Consistent syntax like **lite::cv::Type::Class**, see [examples](#lite.ai.toolkit-Examples-for-Lite.AI.ToolKit). * **Minimum Dependencies.** Only **OpenCV** and **ONNXRuntime** are required by default, see [build](#lite.ai.toolkit-Build-Lite.AI.ToolKit). * **Many Models Supported.** **[300+](#lite.ai.toolkit-Supported-Models-Matrix)** C++ implementations and **[500+](https://github.com/xlite-dev/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.onnx.md)** weights 👉 **[Supported-Matrix](#lite.ai.toolkit-Supported-Models-Matrix)**. ## Build 👇👇 Download prebuilt lite.ai.toolkit library from [tag/v0.2.0](https://github.com/xlite-dev/lite.ai.toolkit/releases/tag/v0.2.0), or just build it from source: ```shell git clone --depth=1 https://github.com/xlite-dev/lite.ai.toolkit.git # latest cd lite.ai.toolkit && sh ./build.sh # >= 0.2.0, support Linux only, tested on Ubuntu 20.04.6 LTS ``` ## Quick Start 🌟🌟 <div id="lite.ai.toolkit-Quick-Start"></div> #### Example0: Object Detection using [YOLOv5](https://github.com/ultralytics/yolov5). Download model from Model-Zoo[<sup>2</sup>](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" int main(int argc, char *argv[]) { std::string onnx_path = "yolov5s.onnx"; std::string test_img_path = "test_yolov5.jpg"; std::string save_img_path = "test_results.jpg"; auto *yolov5 = new lite::cv::detection::YoloV5(onnx_path); std::vector<lite::types::Boxf> detected_boxes; cv::Mat img_bgr = cv::imread(test_img_path); yolov5->detect(img_bgr, detected_boxes); lite::utils::draw_boxes_inplace(img_bgr, detected_boxes); cv::imwrite(save_img_path, img_bgr); delete yolov5; return 0; } ``` You can download the prebuilt lite.ai.tooklit library and test resources from [tag/v0.2.0](https://github.com/xlite-dev/lite.ai.toolkit/releases/tag/v0.2.0). ```bash export LITE_AI_TAG_URL=https://github.com/xlite-dev/lite.ai.toolkit/releases/download/v0.2.0 wget ${LITE_AI_TAG_URL}/lite-ort1.17.1+ocv4.9.0+ffmpeg4.2.2-linux-x86_64.tgz wget ${LITE_AI_TAG_URL}/yolov5s.onnx && wget ${LITE_AI_TAG_URL}/test_yolov5.jpg ``` #### 🎉🎉[TensorRT](https://github.com/NVIDIA/TensorRT): Boost inference performance with NVIDIA GPU via TensorRT. Run `bash ./build.sh tensorrt` to build lite.ai.toolkit with TensorRT support, and then test yolov5 with the codes below. NOTE: lite.ai.toolkit need TensorRT 10.x (or later) and CUDA 12.x (or later). Please check [build.sh](./build.sh), [tensorrt-linux-x86_64-install.zh.md](./docs/tensorrt/tensorrt-linux-x86_64.zh.md), [test_lite_yolov5.cpp](https://github.com/xlite-dev/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolov5.cpp) and [NVIDIA/TensorRT](https://github.com/NVIDIA/TensorRT) for more details. ```c++ // trtexec --onnx=yolov5s.onnx --saveEngine=yolov5s.engine auto *yolov5 = new lite::trt::cv::detection::YOLOV5(engine_path); ``` ## Quick Setup 👀 To quickly setup `lite.ai.toolkit`, you can follow the `CMakeLists.txt` listed as belows. 👇👀 ```cmake set(lite.ai.toolkit_DIR YOUR-PATH-TO-LITE-INSTALL) find_package(lite.ai.toolkit REQUIRED PATHS ${lite.ai.toolkit_DIR}) add_executable(lite_yolov5 test_lite_yolov5.cpp) target_link_libraries(lite_yolov5 ${lite.ai.toolkit_LIBS}) ``` ## Mixed with MNN or ONNXRuntime 👇👇 The goal of lite.ai.toolkit is not to abstract on top of MNN and ONNXRuntime. So, you can use lite.ai.toolkit mixed with MNN(`-DENABLE_MNN=ON, default OFF`) or ONNXRuntime(`-DENABLE_ONNXRUNTIME=ON, default ON`). The lite.ai.toolkit installation package contains complete MNN and ONNXRuntime. The workflow may looks like: ```C++ #include "lite/lite.h" // 0. use yolov5 from lite.ai.toolkit to detect objs. auto *yolov5 = new lite::cv::detection::YoloV5(onnx_path); // 1. use OnnxRuntime or MNN to implement your own classfier. interpreter = std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(mnn_path)); // or: session = new Ort::Session(ort_env, onnx_path, session_options); classfier = interpreter->createSession(schedule_config); // 2. then, classify the detected objs use your own classfier ... ``` The included headers of MNN and ONNXRuntime can be found at [mnn_config.h](./lite/mnn/core/mnn_config.h) and [ort_config.h](./lite/ort/core/ort_config.h). <details> <summary> 🔑️ Check the detailed Quick Start！Click here! </summary> ### Download resources You can download the prebuilt lite.ai.tooklit library and test resources from [tag/v0.2.0](https://github.com/xlite-dev/lite.ai.toolkit/releases/tag/v0.2.0). ```bash export LITE_AI_TAG_URL=https://github.com/xlite-dev/lite.ai.toolkit/releases/download/v0.2.0 wget ${LITE_AI_TAG_URL}/lite-ort1.17.1+ocv4.9.0+ffmpeg4.2.2-linux-x86_64.tgz wget ${LITE_AI_TAG_URL}/yolov5s.onnx && wget ${LITE_AI_TAG_URL}/test_yolov5.jpg tar -zxvf lite-ort1.17.1+ocv4.9.0+ffmpeg4.2.2-linux-x86_64.tgz ``` ### Write test code write YOLOv5 example codes and name it `test_lite_yolov5.cpp`: ```c++ #include "lite/lite.h" int main(int argc, char *argv[]) { std::string onnx_path = "yolov5s.onnx"; std::string test_img_path = "test_yolov5.jpg"; std::string save_img_path = "test_results.jpg"; auto *yolov5 = new lite::cv::detection::YoloV5(onnx_path); std::vector<lite::types::Boxf> detected_boxes; cv::Mat img_bgr = cv::imread(test_img_path); yolov5->detect(img_bgr, detected_boxes); lite::utils::draw_boxes_inplace(img_bgr, detected_boxes); cv::imwrite(save_img_path, img_bgr); delete yolov5; return 0; } ``` ### Setup CMakeLists.txt ```cmake cmake_minimum_required(VERSION 3.10) project(lite_yolov5) set(CMAKE_CXX_STANDARD 17) set(lite.ai.toolkit_DIR YOUR-PATH-TO-LITE-INSTALL) find_package(lite.ai.toolkit REQUIRED PATHS ${lite.ai.toolkit_DIR}) if (lite.ai.toolkit_Found) message(STATUS "lite.ai.toolkit_INCLUDE_DIRS: ${lite.ai.toolkit_INCLUDE_DIRS}") message(STATUS " lite.ai.toolkit_LIBS: ${lite.ai.toolkit_LIBS}") message(STATUS " lite.ai.toolkit_LIBS_DIRS: ${lite.ai.toolkit_LIBS_DIRS}") endif() add_executable(lite_yolov5 test_lite_yolov5.cpp) target_link_libraries(lite_yolov5 ${lite.ai.toolkit_LIBS}) ``` ### Build example ```bash mkdir build && cd build && cmake .. && make -j1 ``` Then, export the lib paths to `LD_LIBRARY_PATH` which listed by `lite.ai.toolkit_LIBS_DIRS`. ```bash export LD_LIBRARY_PATH=YOUR-PATH-TO-LITE-INSTALL/lib:$LD_LIBRARY_PATH export LD_LIBRARY_PATH=YOUR-PATH-TO-LITE-INSTALL/third_party/opencv/lib:$LD_LIBRARY_PATH export LD_LIBRARY_PATH=YOUR-PATH-TO-LITE-INSTALL/third_party/onnxruntime/lib:$LD_LIBRARY_PATH export LD_LIBRARY_PATH=YOUR-PATH-TO-LITE-INSTALL/third_party/MNN/lib:$LD_LIBRARY_PATH # if -DENABLE_MNN=ON ``` ### Run binary: ```bash cp ../yolov5s.onnx ../test_yolov.jpg . ./lite_yolov5 ``` The output logs: ```bash LITEORT_DEBUG LogId: ../examples/hub/onnx/cv/yolov5s.onnx =============== Input-Dims ============== Name: images Dims: 1 Dims: 3 Dims: 640 Dims: 640 =============== Output-Dims ============== Output: 0 Name: pred Dim: 0 :1 Output: 0 Name: pred Dim: 1 :25200 Output: 0 Name: pred Dim: 2 :85 Output: 1 Name: output2 Dim: 0 :1 ...... Output: 3 Name: output4 Dim: 1 :3 Output: 3 Name: output4 Dim: 2 :20 Output: 3 Name: output4 Dim: 3 :20 Output: 3 Name: output4 Dim: 4 :85 ======================================== detected num_anchors: 25200 generate_bboxes num: 48 ``` </details> <div id="lite.ai.toolkit-Supported-Models-Matrix"></div> <!-- <details> <summary> 🔑️ Supported Models Matrix！Click here! </summary> --> ## Supported Models Matrix * / = not supported now. * ✅ = known work and official supported now. * ✔️ = known work, but unofficial supported now. * ❔ = in my plan, but not coming soon, maybe a few months later. ### NVIDIA GPU Inference: TensorRT |Class|Class|Class|Class|Class| System | Engine | |:---:|:---:|:---:|:---:|:---:|:---:|:---:| |✅[YOLOv5](https://github.com/xlite-dev/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolov5.cpp)|✅[YOLOv6](https://github.com/xlite-dev/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolov6.cpp)|✅[YOLOv8](https://github.com/xlite-dev/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolov8.cpp)|✅[YOLOv8Face](https://github.com/xlite-dev/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolov8face.cpp)|✅[YOLOv5Face](https://github.com/xlite-dev/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolo5face.cpp)| Linux | TensorRT | |✅[YOLOX](https://github.com/xlite-dev/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolox.cpp)|✅[YOLOv5BlazeFace](https://github.com/xlite-dev/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolov5_blazeface.cpp) |✅[StableDiffusion](https://github.com/xlite-dev/lite.ai.toolkit/blob/main/examples/lite/sd/test_lite_sd_pipeline.cpp)| ✅[FaceFusion](https://github.com/xlite-dev/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_facefusion_pipeline_gpu.cpp) | / | Linux | TensorRT | ### CPU Inference: ONNXRuntime, MNN, NCNN and TNN | Class | Size | Type | Demo | ONNXRuntime | MNN | NCNN | TNN | Linux | MacOS | Windows | Android | |:-----------------------------------------------------------------------------------------------------------------:|:-----:|:----------------:|:----------------------------------------------------------------------------------------------------------------------:|:-----------:|:---:|:----:|:---:|:-----:|:-----:|:-------:|:-------:| | [YoloV5](https://github.com/ultralytics/yolov5) | 28M | *detection* | [demo](https://github.com/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_yolor.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [ScaledYoloV4](https://github.com/WongKinYiu/ScaledYOLOv4) | 270M | *detection* | [demo](https://github.com/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_rvm.cpp) | ✅ | ✅ | / | ✅ | ✅ | ✔️ | ✔️ | ❔ | | [MGMatting](https://github.com/yucornetto/MGMatting) | 113M | *matting* | [demo](https://github.com/xlite-dev/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/xlite-dev/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_modnet.cpp) | ✅ | ✅ | ✅ | ✅ | ✅ | ✔️ | ✔️ | / | | [MODNetDyn](https://github.com/ZHKKKe/MODNet) | 24M | *matting* | [demo](https://github.com/xlite-dev/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/xlite-dev/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_backgroundmattingv2.cpp) | ✅ | ✅ | / | ✅ | ✅ | ✔️ | ✔️ | / | | [BackgroundMattingV2Dyn](https://github.com/PeterL1n/BackgroundMattingV2) | 20M | *matting* | [demo](https://github.com/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_face_parsing_bisenet_dyn.cpp) | ✅ | / | / | / | / | ✔️ | ✔️ | ❔ | <!-- </details> --> <div id="lite.ai.toolkit-Model-Zoo"></div> <details> <summary> 🔑️ Model Zoo！Click here! </summary> ## Model Zoo. <div id="lite.ai.toolkit-2"></div> **Lite.Ai.ToolKit** contains almost **[100+](https://github.com/xlite-dev/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.onnx.md)** AI models with **[500+](https://github.com/xlite-dev/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.onnx.md)** 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](#lite.ai.toolkit-object-detection)**. More details can be found at [Examples for Lite.Ai.ToolKit](#lite.ai.toolkit-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](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/xlite-dev/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/xlite-dev/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/xlite-dev/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/xlite-dev/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 ``` ### 🔑️ How to download Model Zoo from Docker Hub? * Firstly, pull the image from docker hub. ```shell docker pull qyjdefdocker/lite.ai.toolkit-mnn-hub:v0.1.22.01.08 # (11G) docker pull qyjdefdocker/lite.ai.toolkit-ncnn-hub:v0.1.22.01.08 # (9G) docker pull qyjdefdocker/lite.ai.toolkit-tnn-hub:v0.1.22.01.08 # (11G) docker pull qyjdefdocker/lite.ai.toolkit-onnx-hub:v0.1.22.01.08 # (28G) ``` * 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 The pretrained and converted ONNX files provide by lite.ai.toolkit are listed as follows. Also, see [Model Zoo](#lite.ai.toolkit-Model-Zoo) and [ONNX Hub](https://github.com/xlite-dev/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.onnx.md), [MNN Hub](https://github.com/xlite-dev/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.mnn.md), [TNN Hub](https://github.com/xlite-dev/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.tnn.md), [NCNN Hub](https://github.com/xlite-dev/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.ncnn.md) for more details. </details> <div id="lite.ai.toolkit-Examples-for-Lite.AI.ToolKit"></div> <details> <summary> 🔑️ More Examples！Click here! </summary> ## 🔑️ More Examples. More examples can be found at [examples](https://github.com/xlite-dev/lite.ai.toolkit/tree/main/examples/lite/cv). <div id="lite.ai.toolkit-object-detection"></div> #### Example0: Object Detection using [YOLOv5](https://github.com/ultralytics/yolov5). Download model from Model-Zoo[<sup>2</sup>](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/yolov5s.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_yolov5_1.jpg"; std::string save_img_path = "../../../examples/logs/test_lite_yolov5_1.jpg"; auto *yolov5 = new lite::cv::detection::YoloV5(onnx_path); std::vector<lite::types::Boxf> detected_boxes; cv::Mat img_bgr = cv::imread(test_img_path); yolov5->detect(img_bgr, detected_boxes); lite::utils::draw_boxes_inplace(img_bgr, detected_boxes); cv::imwrite(save_img_path, img_bgr); delete yolov5; } ``` The output is: <div align='center'> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/44dbf4ac-0f38-41b6-930b-55b032b3c2ee' height="256px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/23aca3df-76a6-45c0-a48b-7968b4d4b9d8' height="256px"> </div> Or you can use Newest 🔥🔥 ! YOLO series's detector [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) or [YoloR](https://github.com/WongKinYiu/yolor). They got the similar results. More classes for general object detection (80 classes, 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 !!! ``` **** <div id="lite.ai.toolkit-matting"></div> #### Example1: Video Matting using [RobustVideoMatting2021🔥🔥🔥](https://github.com/PeterL1n/RobustVideoMatting). Download model from Model-Zoo[<sup>2</sup>](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/rvm_mobilenetv3_fp32.onnx"; std::string video_path = "../../../examples/lite/resources/test_lite_rvm_0.mp4"; std::string output_path = "../../../examples/logs/test_lite_rvm_0.mp4"; std::string background_path = "../../../examples/lite/resources/test_lite_matting_bgr.jpg"; auto *rvm = new lite::cv::matting::RobustVideoMatting(onnx_path, 16); // 16 threads std::vector<lite::types::MattingContent> contents; // 1. video matting. cv::Mat background = cv::imread(background_path); rvm->detect_video(video_path, output_path, contents, false, 0.4f, 20, true, true, background); delete rvm; } ``` The output is: <div align='center'> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/a6da4814-6643-4dfc-89ce-57f140c999fc' height="150px" width="150px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/9e4f060e-3de8-44c4-a20f-74a0ff3943bb' height="150px" width="150px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/42bb2991-333a-4524-b874-6ab6156b3425' height="150px" width="150px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/f8d65d8c-2a3d-4634-9169-3bc36452d997' height="150px" width="150px"> <br> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/c1411bb7-5537-4d6e-81f7-c902c2256a72' height="150px" width="150px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/6344f307-15e3-4593-9866-50f5ee777f43' height="150px" width="150px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/4d824828-7727-48df-8aae-64e15ca1c03b' height="150px" width="150px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/e8102fd6-e869-4a42-a19f-dd6d180dda92' height="150px" width="150px"> </div> More classes for matting (image matting, video matting, trimap/mask-free, trimap/mask-based) ```c++ auto *matting = new lite::cv::matting::RobustVideoMatting:(onnx_path); // WACV 2022. auto *matting = new lite::cv::matting::MGMatting(onnx_path); // CVPR 2021 auto *matting = new lite::cv::matting::MODNet(onnx_path); // AAAI 2022 auto *matting = new lite::cv::matting::MODNetDyn(onnx_path); // AAAI 2022 Dynamic Shape Inference. auto *matting = new lite::cv::matting::BackgroundMattingV2(onnx_path); // CVPR 2020 auto *matting = new lite::cv::matting::BackgroundMattingV2Dyn(onnx_path); // CVPR 2020 Dynamic Shape Inference. auto *matting = new lite::cv::matting::MobileHumanMatting(onnx_path); // 3Mb only !!! ``` **** <div id="lite.ai.toolkit-face-alignment"></div> #### Example2: 1000 Facial Landmarks Detection using [FaceLandmarks1000](https://github.com/Single430/FaceLandmark1000). Download model from Model-Zoo[<sup>2</sup>](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/FaceLandmark1000.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_face_landmarks_0.png"; std::string save_img_path = "../../../examples/logs/test_lite_face_landmarks_1000.jpg"; auto *face_landmarks_1000 = new lite::cv::face::align::FaceLandmark1000(onnx_path); lite::types::Landmarks landmarks; cv::Mat img_bgr = cv::imread(test_img_path); face_landmarks_1000->detect(img_bgr, landmarks); lite::utils::draw_landmarks_inplace(img_bgr, landmarks); cv::imwrite(save_img_path, img_bgr); delete face_landmarks_1000; } ``` The output is: <div align='center'> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/318691ec-7226-4d55-990b-a320635d8910' height="224px" width="224px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/d64ae30e-a0b9-4ac9-bf4f-9d6f80c2c05a' height="224px" width="224px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/c802858c-6899-4246-8839-5721c43faffe' height="224px" width="224px"> </div> More classes for face alignment (68 points, 98 points, 106 points, 1000 points) ```c++ auto *align = new lite::cv::face::align::PFLD(onnx_path); // 106 landmarks, 1.0Mb only! auto *align = new lite::cv::face::align::PFLD98(onnx_path); // 98 landmarks, 4.8Mb only! auto *align = new lite::cv::face::align::PFLD68(onnx_path); // 68 landmarks, 2.8Mb only! auto *align = new lite::cv::face::align::MobileNetV268(onnx_path); // 68 landmarks, 9.4Mb only! auto *align = new lite::cv::face::align::MobileNetV2SE68(onnx_path); // 68 landmarks, 11Mb only! auto *align = new lite::cv::face::align::FaceLandmark1000(onnx_path); // 1000 landmarks, 2.0Mb only! auto *align = new lite::cv::face::align::PIPNet98(onnx_path); // 98 landmarks, CVPR2021! auto *align = new lite::cv::face::align::PIPNet68(onnx_path); // 68 landmarks, CVPR2021! auto *align = new lite::cv::face::align::PIPNet29(onnx_path); // 29 landmarks, CVPR2021! auto *align = new lite::cv::face::align::PIPNet19(onnx_path); // 19 landmarks, CVPR2021! ``` **** <div id="lite.ai.toolkit-colorization"></div> #### Example3: Colorization using [colorization](https://github.com/richzhang/colorization). Download model from Model-Zoo[<sup>2</sup>](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/eccv16-colorizer.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_colorizer_1.jpg"; std::string save_img_path = "../../../examples/logs/test_lite_eccv16_colorizer_1.jpg"; auto *colorizer = new lite::cv::colorization::Colorizer(onnx_path); cv::Mat img_bgr = cv::imread(test_img_path); lite::types::ColorizeContent colorize_content; colorizer->detect(img_bgr, colorize_content); if (colorize_content.flag) cv::imwrite(save_img_path, colorize_content.mat); delete colorizer; } ``` The output is: <div align='center'> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/022dd4ab-1048-4d51-8e84-f839464d013e' height="224px" width="224px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/8eceb121-6da6-41d9-8dbf-949034f27247' height="224px" width="224px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/54a9b811-d21b-4120-8381-df0f858dba8b' height="224px" width="224px"> <br> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/95f265a8-ca85-4df1-b2f0-04e1dd3d8fff' height="224px" width="224px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/34bd3bcb-377a-47a0-b8bf-a44603f9b275' height="224px" width="224px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/e1eff9e9-9f3c-4558-8826-d05c0c254e29' height="224px" width="224px"> </div> More classes for colorization (gray to rgb) ```c++ auto *colorizer = new lite::cv::colorization::Colorizer(onnx_path); ``` **** <div id="lite.ai.toolkit-face-recognition"></div> #### Example4: Face Recognition using [ArcFace](https://github.com/deepinsight/insightface/tree/master/recognition/arcface_torch). Download model from Model-Zoo[<sup>2</sup>](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/ms1mv3_arcface_r100.onnx"; std::string test_img_path0 = "../../../examples/lite/resources/test_lite_faceid_0.png"; std::string test_img_path1 = "../../../examples/lite/resources/test_lite_faceid_1.png"; std::string test_img_path2 = "../../../examples/lite/resources/test_lite_faceid_2.png"; auto *glint_arcface = new lite::cv::faceid::GlintArcFace(onnx_path); lite::types::FaceContent face_content0, face_content1, face_content2; cv::Mat img_bgr0 = cv::imread(test_img_path0); cv::Mat img_bgr1 = cv::imread(test_img_path1); cv::Mat img_bgr2 = cv::imread(test_img_path2); glint_arcface->detect(img_bgr0, face_content0); glint_arcface->detect(img_bgr1, face_content1); glint_arcface->detect(img_bgr2, face_content2); if (face_content0.flag && face_content1.flag && face_content2.flag) { float sim01 = lite::utils::math::cosine_similarity<float>( face_content0.embedding, face_content1.embedding); float sim02 = lite::utils::math::cosine_similarity<float>( face_content0.embedding, face_content2.embedding); std::cout << "Detected Sim01: " << sim << " Sim02: " << sim02 << std::endl; } delete glint_arcface; } ``` The output is: <div align='center'> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/8311a1e0-1945-4a70-a361-c15a6e55baab' height="224px" width="224px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/c1619f3f-cb12-4607-9e72-4a9f9224ef09' height="224px" width="224px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/21859087-8458-4be6-b1ab-f20c1546e310' height="224px" width="224px"> </div> > Detected Sim01: 0.721159 Sim02: -0.0626267 More classes for face recognition (face id vector extract) ```c++ auto *recognition = new lite::cv::faceid::GlintCosFace(onnx_path); // DeepGlint(insightface) auto *recognition = new lite::cv::faceid::GlintArcFace(onnx_path); // DeepGlint(insightface) auto *recognition = new lite::cv::faceid::GlintPartialFC(onnx_path); // DeepGlint(insightface) auto *recognition = new lite::cv::faceid::FaceNet(onnx_path); auto *recognition = new lite::cv::faceid::FocalArcFace(onnx_path); auto *recognition = new lite::cv::faceid::FocalAsiaArcFace(onnx_path); auto *recognition = new lite::cv::faceid::TencentCurricularFace(onnx_path); // Tencent(TFace) auto *recognition = new lite::cv::faceid::TencentCifpFace(onnx_path); // Tencent(TFace) auto *recognition = new lite::cv::faceid::CenterLossFace(onnx_path); auto *recognition = new lite::cv::faceid::SphereFace(onnx_path); auto *recognition = new lite::cv::faceid::PoseRobustFace(onnx_path); auto *recognition = new lite::cv::faceid::NaivePoseRobustFace(onnx_path); auto *recognition = new lite::cv::faceid::MobileFaceNet(onnx_path); // 3.8Mb only ! auto *recognition = new lite::cv::faceid::CavaGhostArcFace(onnx_path); auto *recognition = new lite::cv::faceid::CavaCombinedFace(onnx_path); auto *recognition = new lite::cv::faceid::MobileSEFocalFace(onnx_path); // 4.5Mb only ! ``` **** <div id="lite.ai.toolkit-face-detection"></div> #### Example5: Face Detection using [SCRFD 2021](https://github.com/deepinsight/insightface/blob/master/detection/scrfd/). Download model from Model-Zoo[<sup>2</sup>](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/scrfd_2.5g_bnkps_shape640x640.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_face_detector.jpg"; std::string save_img_path = "../../../examples/logs/test_lite_scrfd.jpg"; auto *scrfd = new lite::cv::face::detect::SCRFD(onnx_path); std::vector<lite::types::BoxfWithLandmarks> detected_boxes; cv::Mat img_bgr = cv::imread(test_img_path); scrfd->detect(img_bgr, detected_boxes); lite::utils::draw_boxes_with_landmarks_inplace(img_bgr, detected_boxes); cv::imwrite(save_img_path, img_bgr); delete scrfd; } ``` The output is: <div align='center'> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/b913c502-93fc-4a29-8114-9a3450c512f0' height="224px" width="224px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/28274741-8745-4665-abff-3a384b75f7fa' height="224px" width="224px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/a0bc4d9f-df88-4757-bcfb-214f2c1d4991' height="224px" width="224px"> </div> More classes for face detection (super fast face detection) ```c++ auto *detector = new lite::face::detect::UltraFace(onnx_path); // 1.1Mb only ! auto *detector = new lite::face::detect::FaceBoxes(onnx_path); // 3.8Mb only ! auto *detector = new lite::face::detect::FaceBoxesv2(onnx_path); // 4.0Mb only ! auto *detector = new lite::face::detect::RetinaFace(onnx_path); // 1.6Mb only ! CVPR2020 auto *detector = new lite::face::detect::SCRFD(onnx_path); // 2.5Mb only ! CVPR2021, Super fast and accurate!! auto *detector = new lite::face::detect::YOLO5Face(onnx_path); // 2021, Super fast and accurate!! auto *detector = new lite::face::detect::YOLOv5BlazeFace(onnx_path); // 2021, Super fast and accurate!! ``` **** <div id="lite.ai.toolkit-segmentation"></div> #### Example6: Object Segmentation using [DeepLabV3ResNet101](https://pytorch.org/hub/pytorch_vision_deeplabv3_resnet101/). Download model from Model-Zoo[<sup>2</sup>](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/deeplabv3_resnet101_coco.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_deeplabv3_resnet101.png"; std::string save_img_path = "../../../examples/logs/test_lite_deeplabv3_resnet101.jpg"; auto *deeplabv3_resnet101 = new lite::cv::segmentation::DeepLabV3ResNet101(onnx_path, 16); // 16 threads lite::types::SegmentContent content; cv::Mat img_bgr = cv::imread(test_img_path); deeplabv3_resnet101->detect(img_bgr, content); if (content.flag) { cv::Mat out_img; cv::addWeighted(img_bgr, 0.2, content.color_mat, 0.8, 0., out_img); cv::imwrite(save_img_path, out_img); if (!content.names_map.empty()) { for (auto it = content.names_map.begin(); it != content.names_map.end(); ++it) { std::cout << it->first << " Name: " << it->second << std::endl; } } } delete deeplabv3_resnet101; } ``` The output is: <div align='center'> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/cf143f25-a233-40f1-a4b9-7ad52f691799' height="256px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/f4dd5263-8514-4bb0-a0dd-dbe532481aff' height="256px"> </div> More classes for object segmentation (general objects segmentation) ```c++ auto *segment = new lite::cv::segmentation::FCNResNet101(onnx_path); auto *segment = new lite::cv::segmentation::DeepLabV3ResNet101(onnx_path); ``` **** <div id="lite.ai.toolkit-face-attributes-analysis"></div> #### Example7: Age Estimation using [SSRNet](https://github.com/oukohou/SSR_Net_Pytorch) . Download model from Model-Zoo[<sup>2</sup>](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/ssrnet.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_ssrnet.jpg"; std::string save_img_path = "../../../examples/logs/test_lite_ssrnet.jpg"; auto *ssrnet = new lite::cv::face::attr::SSRNet(onnx_path); lite::types::Age age; cv::Mat img_bgr = cv::imread(test_img_path); ssrnet->detect(img_bgr, age); lite::utils::draw_age_inplace(img_bgr, age); cv::imwrite(save_img_path, img_bgr); delete ssrnet; } ``` The output is: <div align='center'> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/6dc688d9-95be-40f3-b9b8-1a2f69e39e1d' height="224px" width="224px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/77089174-f744-4603-b417-c23caeb344d7' height="224px" width="224px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/8f279483-5849-4356-885c-5806337ced2a' height="224px" width="224px"> </div> More classes for face attributes analysis (age, gender, emotion) ```c++ auto *attribute = new lite::cv::face::attr::AgeGoogleNet(onnx_path); auto *attribute = new lite::cv::face::attr::GenderGoogleNet(onnx_path); auto *attribute = new lite::cv::face::attr::EmotionFerPlus(onnx_path); auto *attribute = new lite::cv::face::attr::VGG16Age(onnx_path); auto *attribute = new lite::cv::face::attr::VGG16Gender(onnx_path); auto *attribute = new lite::cv::face::attr::EfficientEmotion7(onnx_path); // 7 emotions, 15Mb only! auto *attribute = new lite::cv::face::attr::EfficientEmotion8(onnx_path); // 8 emotions, 15Mb only! auto *attribute = new lite::cv::face::attr::MobileEmotion7(onnx_path); // 7 emotions, 13Mb only! auto *attribute = new lite::cv::face::attr::ReXNetEmotion7(onnx_path); // 7 emotions auto *attribute = new lite::cv::face::attr::SSRNet(onnx_path); // age estimation, 190kb only!!! ``` **** <div id="lite.ai.toolkit-image-classification"></div> #### Example8: 1000 Classes Classification using [DenseNet](https://pytorch.org/hub/pytorch_vision_densenet/). Download model from Model-Zoo[<sup>2</sup>](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/densenet121.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_densenet.jpg"; auto *densenet = new lite::cv::classification::DenseNet(onnx_path); lite::types::ImageNetContent content; cv::Mat img_bgr = cv::imread(test_img_path); densenet->detect(img_bgr, content); if (content.flag) { const unsigned int top_k = content.scores.size(); if (top_k > 0) { for (unsigned int i = 0; i < top_k; ++i) std::cout << i + 1 << ": " << content.labels.at(i) << ": " << content.texts.at(i) << ": " << content.scores.at(i) << std::endl; } } delete densenet; } ``` The output is: <div align='center'> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/58e3b905-367d-486a-a3b6-062cef87d726' height="224px" width="350px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/cf81d298-5903-4d3c-ad06-14882911c006' height="224px" width="350px"> </div> More classes for image classification (1000 classes) ```c++ auto *classifier = new lite::cv::classification::EfficientNetLite4(onnx_path); auto *classifier = new lite::cv::classification::ShuffleNetV2(onnx_path); // 8.7Mb only! auto *classifier = new lite::cv::classification::GhostNet(onnx_path); auto *classifier = new lite::cv::classification::HdrDNet(onnx_path); auto *classifier = new lite::cv::classification::IBNNet(onnx_path); auto *classifier = new lite::cv::classification::MobileNetV2(onnx_path); // 13Mb only! auto *classifier = new lite::cv::classification::ResNet(onnx_path); auto *classifier = new lite::cv::classification::ResNeXt(onnx_path); ``` **** <div id="lite.ai.toolkit-head-pose-estimation"></div> #### Example9: Head Pose Estimation using [FSANet](https://github.com/omasaht/headpose-fsanet-pytorch). Download model from Model-Zoo[<sup>2</sup>](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/fsanet-var.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_fsanet.jpg"; std::string save_img_path = "../../../examples/logs/test_lite_fsanet.jpg"; auto *fsanet = new lite::cv::face::pose::FSANet(onnx_path); cv::Mat img_bgr = cv::imread(test_img_path); lite::types::EulerAngles euler_angles; fsanet->detect(img_bgr, euler_angles); if (euler_angles.flag) { lite::utils::draw_axis_inplace(img_bgr, euler_angles); cv::imwrite(save_img_path, img_bgr); std::cout << "yaw:" << euler_angles.yaw << " pitch:" << euler_angles.pitch << " row:" << euler_angles.roll << std::endl; } delete fsanet; } ``` The output is: <div align='center'> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/20a18d56-297c-4c72-8153-76d4380fc9ec)' height="224px" width="224px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/6630a13b-af81-4606-8a81-37fb416f0a64' height="224px" width="224px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/fb873266-2cfd-4b08-8ffb-639aee1ca2b6' height="224px" width="224px"> </div> More classes for head pose estimation (euler angle, yaw, pitch, roll) ```c++ auto *pose = new lite::cv::face::pose::FSANet(onnx_path); // 1.2Mb only! ``` **** <div id="lite.ai.toolkit-style-transfer"></div> #### Example10: Style Transfer using [FastStyleTransfer](https://github.com/onnx/models/tree/master/vision/style_transfer/fast_neural_style). Download model from Model-Zoo[<sup>2</sup>](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/style-candy-8.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_fast_style_transfer.jpg"; std::string save_img_path = "../../../examples/logs/test_lite_fast_style_transfer_candy.jpg"; auto *fast_style_transfer = new lite::cv::style::FastStyleTransfer(onnx_path); lite::types::StyleContent style_content; cv::Mat img_bgr = cv::imread(test_img_path); fast_style_transfer->detect(img_bgr, style_content); if (style_content.flag) cv::imwrite(save_img_path, style_content.mat); delete fast_style_transfer; } ``` The output is: <div align='center'> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/c42af6ea-0b3a-4816-902a-9958fdef5653' height="224px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/bbaa6e7e-50c0-4993-b6e9-aee681e61fdb' height="224px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/95106e7e-c6bc-433d-b20c-95b579e85a06' height="224px"> <br> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/13a17444-27a4-4153-a6ee-5fff0a7fc667' height="224px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/6e2c1d8b-f4a2-4433-b31b-b60f381344c1' height="224px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/9f3f706a-50b7-43e4-8631-13ffa9b12fb5' height="224px"> </div> More classes for style transfer (neural style transfer, others) ```c++ auto *transfer = new lite::cv::style::FastStyleTransfer(onnx_path); // 6.4Mb only ``` **** #### Example11: Human Head Segmentation using [HeadSeg](https://github.com/minivision-ai/photo2cartoon). Download model from Model-Zoo[<sup>2</sup>](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/minivision_head_seg.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_head_seg.png"; std::string save_img_path = "../../../examples/logs/test_lite_head_seg.jpg"; auto *head_seg = new lite::cv::segmentation::HeadSeg(onnx_path, 4); // 4 threads lite::types::HeadSegContent content; cv::Mat img_bgr = cv::imread(test_img_path); head_seg->detect(img_bgr, content); if (content.flag) cv::imwrite(save_img_path, content.mask * 255.f); delete head_seg; } ``` The output is: <div align='center'> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/5684e1d9-b3b1-45af-ac38-d9201490d46e' height="180px" width="180px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/b6a431d2-225b-416b-8a1e-cf9617d79a63' height="180px" width="180px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/ff9740a5-a70e-400c-8301-fc19c92c6248' height="180px" width="180px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/62747811-3856-4f40-9057-9ec4db687b31' height="180px" width="180px"> </div> More classes for human segmentation (head, portrait, hair, others) ```c++ auto *segment = new lite::cv::segmentation::HeadSeg(onnx_path); // 31Mb auto *segment = new lite::cv::segmentation::FastPortraitSeg(onnx_path); // <= 400Kb !!! auto *segment = new lite::cv::segmentation::PortraitSegSINet(onnx_path); // <= 380Kb !!! auto *segment = new lite::cv::segmentation::PortraitSegExtremeC3Net(onnx_path); // <= 180Kb !!! Extreme Tiny !!! auto *segment = new lite::cv::segmentation::FaceHairSeg(onnx_path); // 18M auto *segment = new lite::cv::segmentation::HairSeg(onnx_path); // 18M auto *segment = new lite::cv::segmentation::MobileHairSeg(onnx_path); // 14M ``` **** #### Example12: Photo transfer to Cartoon [Photo2Cartoon](https://github.com/minivision-ai/photo2cartoon). Download model from Model-Zoo[<sup>2</sup>](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string head_seg_onnx_path = "../../../examples/hub/onnx/cv/minivision_head_seg.onnx"; std::string cartoon_onnx_path = "../../../examples/hub/onnx/cv/minivision_female_photo2cartoon.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_female_photo2cartoon.jpg"; std::string save_mask_path = "../../../examples/logs/test_lite_female_photo2cartoon_seg.jpg"; std::string save_cartoon_path = "../../../examples/logs/test_lite_female_photo2cartoon_cartoon.jpg"; auto *head_seg = new lite::cv::segmentation::HeadSeg(head_seg_onnx_path, 4); // 4 threads auto *female_photo2cartoon = new lite::cv::style::FemalePhoto2Cartoon(cartoon_onnx_path, 4); // 4 threads lite::types::HeadSegContent head_seg_content; cv::Mat img_bgr = cv::imread(test_img_path); head_seg->detect(img_bgr, head_seg_content); if (head_seg_content.flag && !head_seg_content.mask.empty()) { cv::imwrite(save_mask_path, head_seg_content.mask * 255.f); // Female Photo2Cartoon Style Transfer lite::types::FemalePhoto2CartoonContent female_cartoon_content; female_photo2cartoon->detect(img_bgr, head_seg_content.mask, female_cartoon_content); if (female_cartoon_content.flag && !female_cartoon_content.cartoon.empty()) cv::imwrite(save_cartoon_path, female_cartoon_content.cartoon); } delete head_seg; delete female_photo2cartoon; } ``` The output is: <div align='center'> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/5684e1d9-b3b1-45af-ac38-d9201490d46e' height="180px" width="180px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/1970b922-6027-44b3-9211-9f057e2ce003' height="180px" width="180px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/73494f60-9efd-48cb-a993-5a5837badb12' height="180px" width="180px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/e10e9624-3176-4137-988b-c73be6103fed' height="180px" width="180px"> </div> More classes for photo style transfer. ```c++ auto *transfer = new lite::cv::style::FemalePhoto2Cartoon(onnx_path); ``` **** #### Example13: Face Parsing using [FaceParsing](https://github.com/zllrunning/face-parsing.PyTorch). Download model from Model-Zoo[<sup>2</sup>](#lite.ai.toolkit-2). ```c++ #include "lite/lite.h" static void test_default() { std::string onnx_path = "../../../examples/hub/onnx/cv/face_parsing_512x512.onnx"; std::string test_img_path = "../../../examples/lite/resources/test_lite_face_parsing.png"; std::string save_img_path = "../../../examples/logs/test_lite_face_parsing_bisenet.jpg"; auto *face_parsing_bisenet = new lite::cv::segmentation::FaceParsingBiSeNet(onnx_path, 8); // 8 threads lite::types::FaceParsingContent content; cv::Mat img_bgr = cv::imread(test_img_path); face_parsing_bisenet->detect(img_bgr, content); if (content.flag && !content.merge.empty()) cv::imwrite(save_img_path, content.merge); delete face_parsing_bisenet; } ``` The output is: <div align='center'> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/32532cbc-ef90-4afb-9fa9-0a1f52b18654' height="180px" width="180px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/157b9e11-fc92-445b-ae0d-0d859c8663ee' height="180px" width="180px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/e7945202-e4dc-4e70-b931-019afdc5a95b' height="180px" width="180px"> <img src='https://github.com/xlite-dev/lite.ai.toolkit/assets/31974251/7dbba712-078a-4cd6-b968-d6f565e10a3e' height="180px" width="180px"> </div> More classes for face parsing (hair, eyes, nose, mouth, others) ```c++ auto *segment = new lite::cv::segmentation::FaceParsingBiSeNet(onnx_path); // 50Mb auto *segment = new lite::cv::segmentation::FaceParsingBiSeNetDyn(onnx_path); // Dynamic Shape Inference. ``` </details> ## ©️License GNU General Public License v3.0 ## 🎉Contribute Please consider ⭐ this repo if you like it, as it is the simplest way to support me. <div align='center'> <a href="https://star-history.com/#xlite-dev/lite.ai.toolkit&Date"> <picture align='center'> <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=xlite-dev/lite.ai.toolkit&type=Date&theme=dark" /> <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=xlite-dev/lite.ai.toolkit&type=Date" /> <img width=450 height=300 alt="Star History Chart" src="https://api.star-history.com/svg?repos=xlite-dev/lite.ai.toolkit&type=Date" /> </picture> </a> </div>