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You should have a look at the verification flow of the FINN build to see where your original model and the FINN transformed/generated model diverge. Normally your final accelerator should produce exactly (up to some small(!) rounding errors) the same outputs as your original ONNX export. The potential rounding errors should not be large enough to significantly reduce your accuracy, so there must be something going wrong here. With the verification flow, FINN checks after certain graph transformations whether the transformed model still produces the expected output for some known input. For an introduction to the built-in verification, you could have a look at the "Verification steps" section of the advanced builder settings notebook: https://github.com/Xilinx/finn/blob/dev/notebooks/advanced/4_advanced_builder_settings.ipynb |
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Hello All,
I would like to clarify a point that I'm stuck on.
Is there any setting, parameter or command of FINN that effects the model accuracy in FPGA?
I trained a custom simplified CNV network with a dataset by using Brevitas QAT. Then the test accuracy is around 95% however after compiling it with FINN and testing it again with the same test dataset in PYNQ-Z2 it gives around 40% accuracy. I could not solve it yet.
I am open to any suggestion.
Thanks in advance,
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