https://github.com/balena-io-examples/balena-openvino
https://github.com/balena-io-examples/balena-openvino
Last synced: 3 months ago
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- Host: GitHub
- URL: https://github.com/balena-io-examples/balena-openvino
- Owner: balena-io-examples
- Created: 2021-05-22T00:29:50.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2022-02-04T17:04:16.000Z (over 3 years ago)
- Last Synced: 2025-01-28T21:35:45.978Z (5 months ago)
- Language: Shell
- Size: 58.6 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# balena-OpenVino
This repo allows you to run OpenVINO AI/ML demos on Intel x86 hardware like a NUC, in a container, managed by balenaCloud. You need a monitor, keyboard/mouse, and USB webcam hooked up to the NUC.
[](https://dashboard.balena-cloud.com/deploy?repoUrl=https://github.com/balena-io-examples/balena-OpenVino)
This is interesting for several reasons, but first and foremost it allows you to remotely update an Edge AI device and push new applications and models to a device no matter where it is located. In this repo, as a sample, we download and install Object Detection and Human Pose Estimation, along with their corresponding models: `pedestrian-detection-adas-0002`, `product-detection-0001`, and `human-pose-estimation-0001`.
You will need to go through the normal balena device deployment steps, as documented here: [https://www.balena.io/docs/learn/getting-started/intel-nuc/python/](https://www.balena.io/docs/learn/getting-started/intel-nuc/python/)
Once your device is online, this repo has been cloned and pushed to the device, and the containers are running, you can hot-swap among the application and the model via Service Variables in the balenaCloud dashboard:
You need to define 3 Service Variables: `DEMO`, `MODEL`, and `ARCHITECTURE_TYPE`
The possible values for `DEMO` are:
```
object_detection_demo
human_pose_estimation_demo
```
For the `MODEL` Service Variable, if you have selected Object Detection, you have a choice of two possible values to enter: `pedestrian-detection-adas-0002` which will detect humans, or `product-detection-0001` which will detect about a dozen or so common grocery items.
If you seledcted Human Pose Estimation, then you only have one choice, and you will need to enter `human-pose-estimation-0001` for the Model.
Finally, the `ARCHITECTURE_TYPE` Variable, has only 1 possible candidate for each. If you selected Object Detection, you will need to enter `ssd` as the value, but if you selected Human Post Estimation then you need to enter `openpose`.
Once your 3 values are entered, your container will start, and you'll see the device do inferencing on the live camera feed!