[tvirosi]

Onnx has really poor support by microsoft. I suspect they've basically abandoned it internally (their onnxjs variant is orders of magnitude slower than tfjs[1]). It's a good 'neutral standard' for the moment but we should all eventually probably move away from it long term.

[1] https://github.com/microsoft/onnxjs/issues/304

[akhundelar]

ONNX != onnxjs

ONNX is a representation format for ML models (mostly neural networks). onnxjs is a just a browser runtime for ONNX models. While it may be true that onnxjs is neglected, please note that the 'main' runtime, onnxruntime, is under heavy active development[1].

Moreover, Microsoft is not the sole steward of the ONNX ecosystem. They are one of many contributors, alongside companies like Facebook, Amazon, Nvidia, and many others [2].

I don't think ONNX is going away anytime soon. Not so sure about the TF ecosystem though.

[1] https://github.com/microsoft/onnxruntime/releases

[2] https://onnx.ai/about.html

[tvirosi]

I've tried inference on the python version of onnx and it usually varies between hitting a OOM limit (while with TF it works fine) to being an order of magnitude slower. Even if the codebase is still being changed I don't see much reason for people to use it other than as a convenient distribution format.

[akhundelar]

Interesting, I did not encounter such discrepancies in my work with these tools.

There could be multiple reasons for the degraded performance:

- Are we comparing apples to apples here (heh), e.g. ResNet-50 vs ResNet-50?

- Was the ONNX model ported from TF? There are known issues with that path (https://onnxruntime.ai/docs/how-to/tune-performance.html#my-...)

- Have you tried tuning an execution provider for your specific target platform?(https://onnxruntime.ai/docs/reference/execution-providers/#s...)