Custom models
In theory, the Nx AI Manager can run any vision model. While it offers seamless support for image classification and object detection, it is also capable of running other model categories, such as:
Image/instance segmentation
Keypoint detection
Vision-language models (VLMs)
Custom model developed from scratch using PyTorch or Tensorflow
For these advanced model types, however, external pre-processing and post-processing are required to:
Prepare and format the model inputs
Interpret and process the model’s raw outputs
If you’re experiencing difficulties running a custom model during integration, please contact the support team for assistance.
Model output types
The following table shows how different model output types appear in the Nx Meta Client and what is required to use them:
Classification
Category + confidence score
Text overlay via event rule
Works out of the box
Object detection
Bounding boxes
Boxes drawn on video feed
Works out of the box
Object counting
Count events
Text overlay / event rule
Requires counting postprocessor
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