Tim M b22c929a64 Corrected additional `.md` files with new image		2 éve
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README.md	b22c929a64 Corrected additional `.md` files with new image	2 éve
object_detection_demo_coco.py	faba3a53fb Files for OpenVINO post 3 Running on IGPU	3 éve
pycocoEvalDemo.ipynb	faba3a53fb Files for OpenVINO post 3 Running on IGPU	3 éve
yolo_to_ssd_classes.py	faba3a53fb Files for OpenVINO post 3 Running on IGPU	3 éve

Running OpenVino Models on Intel Integrated GPU

This repository contains:

Python file to create the results JSON file for the COCO validation dataset.
Juptyter notebook for calculating the mAP.

Instructions

Download the Video Used in the Post

Download the video used in the post for inference from this link.

Getting the JSON Results File

To get the results JSON file for COCO validation set:
- Execute object_detection_demo_coco.py by providing the correct path to the MS COCO validation dataset by editing the Python file.
- Execute using the following commands:
```
python object_detection_demo_coco.py --model fp16/frozen_darknet_yolov4_model.xml -at yolo -i mscoco/val2017 --loop -t 0.2 --no_show -r -nireq 4
```

mAP Calculation

Put the pycocoEvalDemo.ipynb in the cocoapi/PythonAPI.
Run the pycocoEvalDemo.ipynb Notebook by providing the correct path the results.json
The correct path to the MS COCO evaluation JSON file also needs to be provided. Please check the path according to your directory structure of the MS COCO dataset.

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