https://github.com/codait/node-red-contrib-model-asset-exchange
Node-RED nodes for the Model Asset Exchange on IBM Developer
https://github.com/codait/node-red-contrib-model-asset-exchange
node-red-contrib
Last synced: about 1 year ago
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Node-RED nodes for the Model Asset Exchange on IBM Developer
- Host: GitHub
- URL: https://github.com/codait/node-red-contrib-model-asset-exchange
- Owner: CODAIT
- License: apache-2.0
- Created: 2018-12-25T11:31:38.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2020-05-08T14:45:01.000Z (about 6 years ago)
- Last Synced: 2025-05-06T23:16:55.472Z (about 1 year ago)
- Topics: node-red-contrib
- Language: JavaScript
- Homepage: https://developer.ibm.com/exchanges/models/
- Size: 2.51 MB
- Stars: 20
- Watchers: 25
- Forks: 5
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
[](https://travis-ci.org/CODAIT/node-red-contrib-model-asset-exchange) [](https://badge.fury.io/js/node-red-contrib-model-asset-exchange)
Node-RED nodes for deep learning microservices from the [Model Asset eXchange](https://developer.ibm.com/exchanges/models/), providing support for common audio, image, video, and text processing tasks.
## Getting started
To get started follow the [comprehensive tutorial](https://developer.ibm.com/tutorials/learn-how-to-leverage-deep-learning-in-your-node-red-flows/) or complete the quick start steps listed below.
### Setup
#### Docker installation
If you have Docker installed, you can use this [Docker image](https://github.com/CODAIT/max-node-red-docker-image) to try out the examples.
#### Native installation
1. [Install Node-RED](https://nodered.org/docs/getting-started/installation).
> Before you can install Node-RED, you'll need a working install of Node.js. We recommend the use of Node.js LTS 8.x or 10.x, as Node-RED no longer supports Node.js 6.x or earlier.
2. Run the following command in your Node-RED user directory - typically `~/.node-red` to install the [node-red-contrib-model-asset-exchange](https://www.npmjs.com/package/node-red-contrib-model-asset-exchange) module:
$ cd ~/.node-red
$ npm install node-red-contrib-model-asset-exchange
> You can also install the module in the Node-RED editor. Choose **☰** > **Manage palette** > **Install** and enter **model-asset** as the search term.
3. Launch Node-RED and open the displayed URL in a web browser to access the flow editor.
$ node-red
...
... - [info] Server now running at http://127.0.0.1:1880/
4. The nodes are displayed in the palette under the _Model-Asset-eXchange_ category.
### Explore the sample flows
The `node-red-contrib-model-asset-exchange` module includes a couple of example flows to get you started. To import the flows into the workspace:
1. In the Node-RED editor open **☰** > **Import** > **Examples** > **model asset exchange**.
2. Select one of the sub-directories to choose between the basic flows in **getting started**, some more complex examples in **beyond the basics**, or some flows designed to run on the **raspberry pi**.
3. Choose a flow.
> Note: The flows utilize nodes from the [node-red-contrib-browser-util](https://flows.nodered.org/node/node-red-contrib-browser-utils) and [node-red-contrib-image-output](https://flows.nodered.org/node/node-red-contrib-image-output) modules. See the flow description for more details on which nodes are used in a particular example.
You can deploy and run these flows as is. The deep learning nodes in these flows have been pre-configured (service: _cloud_) to connect to hosted evaluation instances of the deep learning microservices.
### Use the nodes in your own flows
Microservice evaluation instances are not suitable for production use. We recommend running microservice instance(s) on your local machine or in the cloud using IBM Cloud Kubernetes, Azure Kubernetes Service, or Google Kubernetes Engine:
1. Deploy the deep learning microservice in the desired environment.
2. Take note of its URL (e.g. `http://localhost:5000`)
3. Add the corresponding deep learning node to your canvas.
4. Open the node properties.
5. Add a service entry for the URL and assign it a unique name.
## Supported application domains
This Node-RED node module supports the following application domains:
- [Image Caption Generator](https://developer.ibm.com/exchanges/models/all/max-image-caption-generator/)
Generate captions that describe the contents of images.
- [Object Detector](https://developer.ibm.com/exchanges/models/all/max-object-detector/)
Localize and identify multiple objects in a single image.
- [Human Pose Estimator](https://developer.ibm.com/exchanges/models/all/max-human-pose-estimator/)
Detect humans in an image and estimate the pose for each person.
- [Audio Classifier](https://developer.ibm.com/exchanges/models/all/max-audio-classifier/)
Identify sounds in short audio clips.
- [Inception ResNet v2](https://developer.ibm.com/exchanges/models/all/max-inception-resnet-v2/)
Identify objects in images using a third-generation deep residual network.
- [Image Segmenter](https://developer.ibm.com/exchanges/models/all/max-image-segmenter/)
Identify objects in an image, additionally assigning each pixel of the image to a particular object.
- [Scene Classifier](https://developer.ibm.com/exchanges/models/all/max-scene-classifier/)
Classify images according to the place/location labels in the Places365 data set.
Note: file inject node in [node-red-contrib-browser-utils](https://flows.nodered.org/node/node-red-contrib-browser-utils) is useful to test these nodes.
License
-------
[Apache-2.0](LICENSE)