Calibrate the deployed representation
ONNX Runtime documents static quantization as using calibration data to determine quantization parameters. Plan that data around the tensors the deployed application will supply. A folder of attractive photographs may miss the effects of the actual camera, crop, resize and numeric transformation. Keep the preprocessing path fixed while collecting or selecting examples. Record which operating conditions the selection represents, such as dark scenes or small distant objects, without implying that a handful of images covers every possible environment. This makes the calibration choice reviewable when a later failure exposes a missing case.
Keep acceptance examples separate
Use a different retained set to judge whether the quantized model still performs the intended task. If the same examples determine conversion settings and provide the only success check, the process leaves little evidence about other inputs. Include cases where the product’s response matters most, and define how output differences will be assessed. For a vision system, that may involve missed objects or unstable locations rather than a single aggregate score. Preserve the unquantized exported model as a reference so a disagreement can be traced to preprocessing, export or quantization. Give important scenarios explicit names in the comparison record. That makes it possible to notice that ordinary daylight images passed while a particular low-light task still needs investigation before deployment.
Version the data choice with the artifact
The converted file is not the entire recipe. Save the calibration selection, preprocessing configuration, tool version and quantization settings under the same release identifier. Review that recipe when the camera, input resolution or environment changes. ONNX Runtime provides accuracy-debugging tools, but they work best when there is a controlled comparison to inspect. If the result misses the application requirement, investigate the affected cases before increasing the calibration set blindly. A useful conversion record explains which inputs informed the artifact, which inputs assessed it and what differences were accepted.
Sources & publication notes
Retrospective period: February 2026. Written and published November 2, 2026. Product announcements establish release timing; support pages are linked as current references.