Tim M b22c929a64 Corrected additional `.md` files with new image		2 năm trước cách đây
..
README.md	b22c929a64 Corrected additional `.md` files with new image	2 năm trước cách đây
object_detection_demo_coco.py	6ffa740e27 introduction to openvino deep learning workbench	3 năm trước cách đây
pycocoEvalDemo.ipynb	6ffa740e27 introduction to openvino deep learning workbench	3 năm trước cách đây
yolo_to_ssd_classes.py	6ffa740e27 introduction to openvino deep learning workbench	3 năm trước cách đây

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.

AI Courses by OpenCV

Want to become an expert in AI? AI Courses by OpenCV is a great place to start.