![]() ![]() To address this gap, we introduce Zenseact Open Dataset (ZOD), a Long-range capabilities, focusing instead on 360° perception and temporal If you want to follow along with the version of the code used for this post, take a look at the source on github:įansi.Download a PDF of the paper titled Zenseact Open Dataset: A large-scale and diverse multimodal dataset for autonomous driving, by Mina Alibeigi and 9 other authors Download PDF Abstract: Existing datasets for autonomous driving (AD) often lack diversity and The Fansi documentation has a lot more to say about why Fansi exists, but this should have given you a flavor of the problem it's trying to solve Micro-optimized: How Fansi WorksĪt its core, Fansi is currently built on three data-structures: This is a tiny library that I wrote to make it easier to deal with color-coded Ansi strings: The Use Case: FansiĪs a real-world use case to demonstrate these techniques, I am going to use the Fansi library. Then roll back the optimizations one by one in order to see what kind of performance impact they had.īy the end, you should have a good sense of what these micro-optimization techniques are, what benefit they provide, and where they could possibly be used in your own code. We will start off with a tour of the already-optimized Fansi libraryĭiscuss the internals and highlight the micro-optimizations that are meant to make Fansi fast ![]() Thus, this post will take the opposite tack: The Fansi library has already been optimized, and thus I have already gone through this process, identified the various bottlenecks, and optimized them one by one. In this case, it is the latter, and we are done micro-optimizing render.
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