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@DefTruth
DefTruth committed Mar 18, 2024
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Expand Up @@ -111,7 +111,7 @@ add_executable(lite_yolov5 examples/test_lite_yolov5.cpp)
target_link_libraries(lite_yolov5 ${lite.ai.toolkit_LIBS})
```
<details>
<summary> 🔑️ Supported Models Matrix </summary>
<summary> 🔑️ Supported Models Matrix！Click here! </summary>

## Supported Models Matrix
<div id="lite.ai.toolkit-Supported-Models-Matrix"></div>
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</details>


<details>
<summary> 🔑️ Model Zoo！Click here! </summary>

## Model Zoo.

<div id="lite.ai.toolkit-Model-Zoo"></div>
Expand All @@ -253,8 +256,7 @@ target_link_libraries(lite_yolov5 ${lite.ai.toolkit_LIBS})
docker pull qyjdefdocker/lite.ai.toolkit-tnn-hub:v0.1.22.02.02 # (217M) + YOLO5Face
```

<details>
<summary> 🔑️ How to download Model Zoo from Docker Hub?</summary>
### 🔑️ How to download Model Zoo from Docker Hub?

* Firstly, pull the image from docker hub.
```shell
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cp -rf mnn/cv share/
```

</details>

### 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/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) for more details.

</details>


## Examples.

Expand Down Expand Up @@ -975,81 +978,13 @@ auto *segment = new lite::cv::segmentation::FaceParsingBiSeNet(onnx_path); // 50
auto *segment = new lite::cv::segmentation::FaceParsingBiSeNetDyn(onnx_path); // Dynamic Shape Inference.
```

## License.
## License

<div id="lite.ai.toolkit-License"></div>

The code of [Lite.Ai.ToolKit](#lite.ai.toolkit-Introduction) is released under the GPL-3.0 License.


## References.

<div id="lite.ai.toolkit-References"></div>

Many thanks to these following projects. All the Lite.AI.ToolKit's models are sourced from these repos.

* [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) (🔥🔥🔥↑)

<details>
<summary> Expand for More References.</summary>

* [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) (🔥🔥↑)

</details>


## Compilation Options.

In addition, [MNN](https://github.com/alibaba/MNN), [NCNN](https://github.com/Tencent/ncnn) and [TNN](https://github.com/Tencent/TNN) support for some models will be added in the future, but due to operator compatibility and some other reasons, it is impossible to ensure that all models supported by [ONNXRuntime C++](https://github.com/microsoft/onnxruntime) can run through [MNN](https://github.com/alibaba/MNN), [NCNN](https://github.com/Tencent/ncnn) and [TNN](https://github.com/Tencent/TNN). So, if you want to use all the models supported by this repo and don't care about the performance gap of *1~2ms*, just let [ONNXRuntime](https://github.com/microsoft/onnxruntime) as default inference engine for this repo. However, you can follow the steps below if you want to build with [MNN](https://github.com/alibaba/MNN), [NCNN](https://github.com/Tencent/ncnn) or [TNN](https://github.com/Tencent/TNN) support.

* change the `build.sh` with `DENABLE_MNN=ON`,`DENABLE_NCNN=ON` or `DENABLE_TNN=ON`, such as
```shell
cd build && cmake \
-DCMAKE_BUILD_TYPE=MinSizeRel \
-DINCLUDE_OPENCV=ON \ # Whether to package OpenCV into lite.ai.toolkit, default ON; otherwise, you need to setup OpenCV yourself.
-DENABLE_MNN=ON \ # Whether to build with MNN, default OFF, only some models are supported now.
-DENABLE_NCNN=OFF \ # Whether to build with NCNN, default OFF, only some models are supported now.
-DENABLE_TNN=OFF \ # Whether to build with TNN, default OFF, only some models are supported now.
.. && make -j8
```
* use the MNN, NCNN or TNN version interface, see [demo](https://github.com/DefTruth/lite.ai.toolkit/blob/main/examples/lite/cv/test_lite_nanodet.cpp), such as
```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. Contribute
## Contribute
<div id="lite.ai.toolkit-Contribute"></div>

How to add your own models and become a contributor? See [CONTRIBUTING.zh.md](https://github.com/DefTruth/lite.ai.toolkit/issues/191).
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