Not a duckdb user, but I use polars a lot (mentioned in the article).
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.
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.
As far as my understanding goes, you can apply a distance measure on the (neural)hashes of the input image and the reference image. A proximity threshold will determine if it's a variant of the original image or not. So, it should probably be pretty good at defeating basic evasion.
It is not like a cryptographic hash, where altering a single bit will completely change the output.
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.