coreml-validate

To validate CoreML model accuracy against a PyTorch reference model, use numerical parity validation to compare outputs, providing detailed correlation and error analysis to ensure consistent results.

Yes, you can compare both FP16 and FP32 precision outputs during Core ML model validation. Multi-precision comparison allows you to assess accuracy variations across different floating-point formats.

To debug numerical discrepancies in a Core ML inference pipeline, run a full pipeline comparison against your PyTorch reference to isolate and identify exactly where output mismatches occur at each stage.

Numerical parity validation for Core ML models is the process of comparing Core ML outputs against PyTorch reference outputs to verify that the converted model maintains consistent and reliable numerical accuracy.

You test a Core ML model pipeline against a PyTorch reference by triggering validation with your model file and reference path, which validates the full inference pipeline to isolate any stage discrepancies.