I’m training my own mushroom-classification model and plan to run it offline on a Raspberry Pi Compute Module 5 with the CM5 IO Board. Storage is the official Raspberry Pi NVMe SSD.
What is the best way to optimize a TensorFlow Lite model for fast inference on this setup?
I’m especially interested in:
recommended quantization type for CM5 performance,
ideal input image size for a small classifier,
whether ONNX Runtime or TFLite gives better speed on CM5,
ways to reduce CPU load during repeated inference,
anything specific to running the model from the official SSD instead of SD,
Any practical tips from people who have deployed custom vision models on CM4/CM5 would be appreciated.
What is the best way to optimize a TensorFlow Lite model for fast inference on this setup?
I’m especially interested in:
recommended quantization type for CM5 performance,
ideal input image size for a small classifier,
whether ONNX Runtime or TFLite gives better speed on CM5,
ways to reduce CPU load during repeated inference,
anything specific to running the model from the official SSD instead of SD,
Any practical tips from people who have deployed custom vision models on CM4/CM5 would be appreciated.
Statistics: Posted by Kirill_Pegankin — Wed Nov 26, 2025 1:34 pm — Replies 0 — Views 7