tf-lite micro library has many advantages, and the first of these is the tensorFlow framework itself. You can train the model easily and then implement the same or a similar architecture on esp-32s without much effort. Another advantage is its optimization and you can easily intervene in various memory optimizations and even though it is not a large one, it does have a community.
Apart from these, for example, the author implemented the model the traditional way using C, but it is more convenient to use tf-lite micro on esp32s with the Berry script language.
However, since I have never used onnxin this kind of project, I cant speak to its advantages, so comparisons are difficult from my perspective. But as I said, tf-lite and offer benefits like easy integration, good optimization, and as the name implies, tensorFlow.
Apart from these, for example, the author implemented the model the traditional way using C, but it is more convenient to use tf-lite micro on esp32s with the Berry script language.
However, since I have never used onnxin this kind of project, I cant speak to its advantages, so comparisons are difficult from my perspective. But as I said, tf-lite and offer benefits like easy integration, good optimization, and as the name implies, tensorFlow.