Convert ONNX, TFLite, PB, and PyTorch models to QNN context binaries.
npx skills add https://github.com/Pico-Developer/pySecureMR --skill spatialml
Please help me install this Agent Skill. Skill: spatialml Source: https://github.com/Pico-Developer/pySecureMR/tree/main/skills/spatialml Command: npx skills add https://github.com/Pico-Developer/pySecureMR --skill spatialml
This Skill streamlines the process of converting machine learning models into a format compatible with Pico devices and provides tools for debugging these models on the device.
.serialized.bin format for your Pico headset and then use the model inspector to verify its performance on the device.Use the spatialml skill to convert the attached model file 'my_model.onnx' to a QNN context binary.
High-intent search queries and answers about installing and using this skill.
To convert an ONNX or PyTorch model to a QNN context binary for Pico devices, you use a conversion process that requires Docker. This transforms your model into a .serialized.bin format for headset deployment.
Yes, you can debug a machine learning model directly on a Pico headset using Python-based inspection tools. This requires ADB for on-device interaction to evaluate model and SecureMR pipeline performance.
Supported formats for ML model conversion to Pico devices include ONNX, TFLite, PB, and PyTorch. These models are converted into QNN context binaries to run on the target hardware.
Yes, you need Docker to perform the ML model conversion to QNN context binaries and ADB to facilitate on-device debugging and interaction with the Pico device.
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