Overview Object Segmentation Node

Overview Object Segmentation Node

The Object Segmentation Node is used to segment several different objects off the shelf with pre-trained or custom deep learning models on GPU, CPU, VPU (Myriad) and TPU.

Input and Output
  1. Input: Frame from a video file, IP or USB camera.
  2. Output: MQTT message containing the segmentation results.
  3. Supported architecture: Currently supported on amd64 devices.
Node Parameters
The following parameters are used in the Object Segmentation node.

Name: Input the node name used in a specific flow.
  1. default: object-segmentation
  2. type: string
Device: Select the target device mode to be used. Currently available are CPU, Nvidia GPU, VPU (Myriad) and TPU.
  1. default: CPU
  2. type: string
In case you select VPU as your target device, you will be displayed with an additional field where you can indicate how many VPU target devices you would like to run your models on, or run the edge inference on both CPU and VPU. To do that, simply enter MYRIAD,MYRIAD,CPU as an example. This means, that the inference will be performed on 2x VPUs and 1x CPU.

Model Name: Select one of the most popular public models for object segmentation.
  1. Available models: See here
  2. type: string
Custom Model: You can link your own custom model by adding the model URL.
Detection Labels: Select the target object(s) to be segmented. A single or multiple objects can be selected.
  1. default: none
  2. type: string
Detection Score Threshold: Refers to the threshold value for confidence score filtering.
  1. default: 0.5
  2. range: (0.0~1.0)

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