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Deep Learning Classification Node
Overview
The DL Classification Node offers state-of-the-art classification using pre-trained models. Deep learning classification can be performed on any point cloud or image.
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Input and Output
Input |
Type |
Description |
---|---|---|
Data Input |
Image / Point Cloud |
The RGB image / point cloud used for classification (Camera, Reader, DL Segment etc.). |
Use GPU Model |
Bool |
Use the GPU Model when true. |
Config File Path |
String |
The path to deep learning config file. |
Output |
Type |
Description |
---|---|---|
classLabel |
int |
The class label for the most confident prediction. |
confidence |
double |
The score for the most confident class from 0 to 1. |
Node Settings
Data Source
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- Data Input:
- The input scene to generate the prediction from.
For RGBD, use point cloud.
For RGB, use image or point cloud.
For DEPTH, use depth image or point cloud.
Classification Settings
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Please refer to the Deep Learning page for instructions on collecting dataset and training a model.
- Use GPU Model (Default: false):
Use the GPU Model when true.
- Config File Path:
The file path for the .txt deep learning config file.
Procedure to Use
Insert Camera, and DL Classify node.
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Put the classification model files into the project’s Data file. You can use the data here .
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Set up the Camera node. Please refer to Camera Node for more instructions on creating a virtual camera.
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Click the DL Classify node. Link the Camera output and select the config file.
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Run the two nodes. You can see the predicted class and probability from the label.
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