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DefTruth committed Sep 19, 2021
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</div>


*Lite.AI.ToolKit* 🚀🚀🌟: A lite C++ toolkit of awesome AI models which contains *[70+](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.onnx.md)* models now. It's a collection of personal interests. Such as YOLOX, YOLOP, YOLOR, YoloV5, YoloV4, DeepLabV3, ArcFace, etc. *Lite.AI.ToolKit* based on *[onnxruntime](https://github.com/microsoft/onnxruntime)* by default. I do have plans to reimplement it with *[ncnn](https://github.com/Tencent/ncnn)* and *[MNN](https://github.com/alibaba/MNN)*, but not coming soon. It includes [object detection](#lite.ai.toolkit-object-detection), [face detection](#lite.ai.toolkit-face-detection), [face alignment](#lite.ai.toolkit-face-alignment), [face recognition](#lite.ai.toolkit-face-recognition), [segmentation](#lite.ai.toolkit-segmentation), [colorization](#lite.ai.toolkit-colorization), [matting](#lite.ai.toolkit-matting), etc. You can use these awesome models simply through *lite::cv::Type::Class* syntax, such as *[lite::cv::detection::YoloV5](#lite.ai.toolkit-object-detection)*.
*Lite.AI.ToolKit* 🚀🚀🌟: A lite C++ toolkit of awesome AI models which contains *[70+](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.onnx.md)* models now. It's a collection of personal interests. Such as YOLOX, YOLOP, YOLOR, YoloV5, YoloV4, DeepLabV3, ArcFace, etc. *Lite.AI.ToolKit* based on *[onnxruntime](https://github.com/microsoft/onnxruntime)* by default. I do have plans to reimplement it with *[ncnn](https://github.com/Tencent/ncnn)* and *[MNN](https://github.com/alibaba/MNN)*, but not coming soon. The core features of *Lite.AI.ToolKit* are listed as follows.

* Simply and User friendly.
To use Lite.AI.ToolKit, you do not need to have a lot of AI skills, C++ is enough. You can use these awesome models simply through *lite::cv::Type::Class* syntax, see [examples](#lite.ai.toolkit-Examples-for-Lite.AI.ToolKit).
```c++
auto *yolox = new lite::cv::detection::YoloX("yolox_nano.onnx"); // 3.5Mb only !
auto *yolov5 = new lite::cv::detection::YoloV5("yolov5s.onnx"); // for mobile device
```

* Minimum Dependencies & Easy to build.
Currently, Lite.AI.ToolKit only depends on OpencV and ONNXRuntime. 🚀 You can download the latest *ONNXRuntime* official built libs from [v1.8.1](https://github.com/microsoft/onnxruntime/releases), [v1.7.0](https://github.com/microsoft/onnxruntime/releases/tag/v1.7.0). No more attentions needed pay to build it from source. On MacOS, it takes only a few lines of command to build Lite.AI.ToolKit, see [build](#lite.ai.toolkit-Build-Lite.AI.ToolKit).
```shell
git clone --depth=1 https://github.com/DefTruth/lite.ai.toolkit.git # latest
cd lite.ai.toolkit
sh ./build.sh # On MacOS, you can use the built OpenCV and ONNXRuntime libs in this repo.
```

* Cross-platform support.
Lite.AI.ToolKit support MacOS/Linux/Windows and CPU/GPU now. More platforms may be supported in the future ~


* Lots of Algorithm Modules.
Currently, Lite.AI.ToolKit includes 10+ modules for Computer Vision, see [model zoo](#lite.ai.toolkit-Model-Zoo). Such as:
* [image classification](#lite.ai.toolkit-image-classification)
* [object detection](#lite.ai.toolkit-object-detection)
* [face detection](#lite.ai.toolkit-face-detection)
* [face alignment](#lite.ai.toolkit-face-alignment)
* [face recognition](#lite.ai.toolkit-face-recognition)
* [face attributes analysis](#lite.ai.toolkit-face-attributes-analysis)
* [head pose estimation](#lite.ai.toolkit-head-pose-estimation)
* [segmentation](#lite.ai.toolkit-segmentation)
* [colorization](#lite.ai.toolkit-colorization)
* [style transfer](#lite.ai.toolkit-style-transfer)
* [matting](#lite.ai.toolkit-matting)



## Citations.

Cite it as follows if you use *Lite.AI.ToolKit*. Note, More models will continue to be added ~
Cite it as follows if you use *Lite.AI.ToolKit*. Watch 👀👆🏻 this repo, more models will continue to be added ~
```BibTeX
@misc{lite.ai.toolkit2021,
title={lite.ai.toolkit: A lite C++ toolkit of awesome AI models.},
Expand Down Expand Up @@ -114,25 +149,6 @@ install `OpenCV` and `onnxruntime` libraries using Homebrew or you can download
brew install onnxruntime
```

<details>
<summary> Expand for More Details of Dependencies.</summary>
### Linux.
* *todo*⚠️
### Windows.
* *todo*⚠️
### Inference Engine Plans:
* *doing*:
❇️ `onnxruntime`
* *todo*:
⚠️ `NCNN`
⚠️ `MNN`
⚠️ `OpenMP`
</details>
---->

## 1. Build Lite.AI.ToolKit
Expand Down

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