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

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.