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@DefTruth
DefTruth committed Mar 18, 2024
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1 change: 0 additions & 1 deletion LICENSE
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Expand Up @@ -672,4 +672,3 @@ may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.

59 changes: 1 addition & 58 deletions README.md
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Expand Up @@ -110,65 +110,8 @@ find_package(lite.ai.toolkit REQUIRED PATHS ${LITE_AI_DIR})
add_executable(lite_yolov5 examples/test_lite_yolov5.cpp)
target_link_libraries(lite_yolov5 ${lite.ai.toolkit_LIBS})
```


## ❇️ Lite.Ai.ToolKit modules.

### Namespace and Lite.Ai.ToolKit modules.

| 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's Classes and Pretrained Files.

Correspondence between the classes in **Lite.AI.ToolKit** and pretrained model files can be found at [lite.ai.toolkit.hub.onnx.md](https://github.com/DefTruth/lite.ai.toolkit/tree/main/docs/hub/lite.ai.toolkit.hub.onnx.md). For examples, the pretrained model files for *lite::cv::detection::YoloV5* and *lite::cv::detection::YoloX* are listed as follows.


| 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 |

It means that you can load the the any one `yolov5*.onnx` and `yolox_*.onnx` according to your application through the same Lite.AI.ToolKit's classes, such as *YoloV5*, *YoloX*, etc.

```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 !
```

<details>
<summary> ❇️ Supported Models Matrix </summary>
<summary> 🔑️ Supported Models Matrix </summary>

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