akhundelar
Depends on your definition of medium sized, but for tables of hundreds of thousands of rows and ~30 columns, these tools are fast enough to run queries instantly or near instantly even on laptop CPUs.
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...)
What I had in mind when referring to 'basic evasion' was 'cropping or rotating', as per your original comment.
All that being said, I admit that generating adversarial examples for models with known weights is not a difficult task.
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.
It is not like a cryptographic hash, where altering a single bit will completely change the output.