chore(demo): add ncnn cpp README

demo/ncnn/cpp/README.md

@@ -0,0 +1,83 @@
+# YOLOX-CPP-NCNN
+
+Cpp file compile of YOLOX object detection base on [ncnn](https://github.com/Tencent/ncnn).
+
+## Tutorial
+
+### Step1
+Clone [ncnn](https://github.com/Tencent/ncnn) first, then please following [build tutorial of ncnn](https://github.com/Tencent/ncnn/wiki/how-to-build) to build on your own device.
+
+### Step2
+Use provided tools to generate onnx file.
+For example, if you want to generate onnx file of yolox-s, please run the following command:
+```shell
+cd <path of yolox>
+python3 tools/export_onnx.py -n yolox-s
+```
+Then, a yolox.onnx file is generated.
+
+### Step3
+Generate ncnn param and bin file.
+```shell
+cd <path of ncnn>
+cd build/tools/ncnn
+./onnx2ncnn yolox.onnx yolox.param yolox.bin
+```
+
+Since Focus module is not supported in ncnn. Warnings like:
+```shell
+Unsupported slice step ! 
+```
+will be printed. However, don't  worry!  C++ version of Focus layer is already implemented in yolox.cpp.
+
+### Step4
+Open **yolox.param**, and modify it.
+Before (just an example):
+```
+295 328
+Input            images                   0 1 images
+Split            splitncnn_input0         1 4 images images_splitncnn_0 images_splitncnn_1 images_splitncnn_2 images_splitncnn_3
+Crop             Slice_4                  1 1 images_splitncnn_3 647 -23309=1,0 -23310=1,2147483647 -23311=1,1
+Crop             Slice_9                  1 1 647 652 -23309=1,0 -23310=1,2147483647 -23311=1,2
+Crop             Slice_14                 1 1 images_splitncnn_2 657 -23309=1,0 -23310=1,2147483647 -23311=1,1
+Crop             Slice_19                 1 1 657 662 -23309=1,1 -23310=1,2147483647 -23311=1,2
+Crop             Slice_24                 1 1 images_splitncnn_1 667 -23309=1,1 -23310=1,2147483647 -23311=1,1
+Crop             Slice_29                 1 1 667 672 -23309=1,0 -23310=1,2147483647 -23311=1,2
+Crop             Slice_34                 1 1 images_splitncnn_0 677 -23309=1,1 -23310=1,2147483647 -23311=1,1
+Crop             Slice_39                 1 1 677 682 -23309=1,1 -23310=1,2147483647 -23311=1,2
+Concat           Concat_40                4 1 652 672 662 682 683 0=0
+...
+```
+* Change first number for 295 to 295 - 9 = 286(since we will remove 10 layers and add 1 layers, total layers number should minus 9). 
+* Then remove 10 lines of code from Split to Concat, but remember the last but 2nd number: 683.
+* Add YoloV5Focus layer After Input (using previous number 683):
+```
+YoloV5Focus      focus                    1 1 images 683
+```
+After(just an exmaple):
+```
+286 328
+Input            images                   0 1 images
+YoloV5Focus      focus                    1 1 images 683
+...
+```
+
+### Step5
+Use onnx_optimze to generate new param and bin:
+```shell
+# suppose you are still under ncnn/build/tools/ncnn dir.
+../ncnnoptimize model.param model.bin yolox.param yolox.bin 65536
+```
+
+### Step6
+Copy or Move yolox.cpp file into ncnn/examples, modify the CMakeList.txt, then build yolox
+
+### Step7
+Inference image with executable file yolox, enjoy the detect result:
+```shell
+./yolox demo.jpg
+```
+
+## Acknowledgement
+
+* [ncnn](https://github.com/Tencent/ncnn)