learnopencv/Introduction-to-OpenVino-Deep-Learning-Workbench

Introduction to OpenVino Deep Learning Workbench

This repository contains code for Introduction-to-OpenVino-Deep-Learning Workbench blogpost.

And the following as well,

• 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 frozen_darknet_yolov4_model.xml -at yolo -i mscoco/val2017 --loop -t 0.2 --no_show -r -nireq 4
  

  • Note: Check that the path to the .xml file is for the INT8 model.

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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