sample notebooks for yolov4 and tiny-yoloV2

python/OpenVINO_EP/yolov7_object_detection_fp32_int8/README.md

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+# Object detection with YOLOv7 in Python using OpenVINO™ Execution Provider
+## Virtual Environement
+Create a virtual environment either using a the `conda` virtual environment or `python venv`.
+
+### Python venv
+```
+$ python3 -m venv yv7-pyenv
+$ source yv7-pyenv/bin/activate
+$ pip install --upgrade pip
+```
+### Conda
+If system python3 version is < 3.8.13 it's recommended to either upgrade to python3.8 or use create virtual environment python3.8 using conda.
+```
+$ conda create -n yolov7 python=3.8
+$ conda activate yolov7
+```
+## Install required packages
+After creating and activating virtual environment, install the required packages:
+
+### NNCF Experimental
+
+```
+$ git clone https://github.com/openvinotoolkit/nncf.git
+$ cd nncf && python setup.py develop --onnx
+$ pip install --no-cache-dir onnxconverter_common onnxruntime-openvino==1.11.0
+$ cd .. && rm -fR nncf 
+```
+
+### YoloV7 requirements
+
+```
+$ cd notebooks
+$ git clone https://github.com/WongKinYiu/yolov7
+$ cp -R yolov7/* .
+$ pip install --no-cache-dir -r requirements.txt
+$ rm -fR yolov7
+```
+
+Launch the notebook from here.
+## Dataset
+The dataset used in this sample is the coco-validation2017 dataset. It will be downloaded automatically in the desired location while running the notebook.
+
+## Outputs
+
+- `yolov7-tiny.onnx` 
+- `yolov7-tiny-quantized.onnx`
+- `cat-yolov7-detected.jpg`
+
+## References
+### [ONNX Rruntime](https://onnxruntime.ai/docs/install/)
+### [NNCF](https://github.com/openvinotoolkit/nncf/tree/develop)