Velox Neuro - Neural Network Inference Engine

**** UPDATE ****

Velox Neuro plugin version 0.69b Update:

• Added possibility to access tensor internal data via blueprint functions

Velox Neuro plugin version 0.65b Update:

• Mac Platform Support
• Android Platform Support
• General performance / stability update
• Fixed FillTextureFromTensor / ConvertTensorToTexture to support tensors with more than 1 channel
• Added SaveTensorAsImage to save tensors as PNG file
• Added PermuteTensorChannels function
• Added support for tensors without batching information
• Added support for single channel textures in ConvertTextureToTensor
• ONNX Runtime updated to version 1.16.2
• Default search path "MLModels" in the Project's Content for standalone builds

Velox Neuro is an open-source code plug-in that provides a set of utilities for running inference using ONNX Runtime native library.

This plugin supports ONNX (Open Neural Network Exchange), which is an open-source machine learning model format widely used.

1. Create a Blueprint
2. Create an instance of Inference Engine with your chosen ONNX model file
3. Assign Tensor Inputs from Textures
4. Execute Inference
5. Get Output as Textures

Many ML frameworks such as PyTorch and TensorFlow can export its model in ONNX format.

Many trained models are available on ONNX Model Zoo.

Get example ONNX files from here:

• MiDaS_3_1_Swin2_L.onnx
• MiDaS_3_1_Swin2_T.onnx
• rvm_mobilenetv3_fp32.onnx
• rvm_resnet50_fp32.onnx
• yolov7.onnx

Download our Demo Project (compatible with 5.1, 5.2, 5.3) with example of loading any media sources and running inference with:

• Object Detection from a video returning a 2D Region of Interest (RoI)
• Mask Generation
• Depth estimation
• Real-time mesh animation with depth projection

Read this Documentation before using the Demo Project.

Video of using a webcam in the Demo Project with Velox Neuro.

Velox Neuro plugin is used by Velox Player Plus plugin for importing volumetric videos in the VLX format taken by iOS devices with LiDAR.

The plugin supports running inference on Windows using:

• CPU
• CUDA
• DirectML

Visit our Product page.