ultralytics 8.0.41 TF SavedModel and EdgeTPU export (#1034)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Noobtoss <96134731+Noobtoss@users.noreply.github.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>

examples/YOLOv8-CPP-Inference/README.md

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+# yolov8/yolov5 Inference C++
+
+Usage:
+
+```
+# git clone ultralytics
+pip install .
+cd examples/cpp_
+
+Add a **yolov8\_.onnx** and/or **yolov5\_.onnx** model(s) to the ultralytics folder.
+Edit the **main.cpp** to change the **projectBasePath** to match your user.
+
+Note that by default the CMake file will try and import the CUDA library to be used with the OpenCVs dnn (cuDNN) GPU Inference.
+If your OpenCV build does not use CUDA/cuDNN you can remove that import call and run the example on CPU.
+
+mkdir build
+cd build
+cmake ..
+make
+./Yolov8CPPInference
+```
+
+To export yolov8 models:
+
+```
+yolo export \
+model=yolov8s.pt \
+imgsz=[480,640] \
+format=onnx \
+opset=12
+```
+
+To export yolov5 models:
+
+```
+python3 export.py \
+--weights yolov5s.pt \
+--img 480 640 \
+--include onnx \
+--opset 12
+```
+
+yolov8s.onnx:
+
+![image](https://user-images.githubusercontent.com/40023722/217356132-a4cecf2e-2729-4acb-b80a-6559022d7707.png)
+
+yolov5s.onnx:
+
+![image](https://user-images.githubusercontent.com/40023722/217357005-07464492-d1da-42e3-98a7-fc753f87d5e6.png)
+
+This repository is based on OpenCVs dnn API to run an ONNX exported model of either yolov5/yolov8 (In theory should work
+for yolov6 and yolov7 but not tested). Note that for this example the networks are exported as rectangular (640x480)
+resolutions, but it would work for any resolution that you export as although you might want to use the letterBox
+approach for square images depending on your use-case.
+
+The **main** branch version is based on using Qt as a GUI wrapper the main interest here is the **Inference** class file
+which shows how to transpose yolov8 models to work as yolov5 models.