TensorFlow Lite
Low-latency inference of on-device ML models
TensorFlow Lite launched on November 14, 2017 with 163 votes, #219 of 3,137 launches that month and more than 90% of that year's launches. The site was still online in September 2026, 8 years on.
What it does
TensorFlow’s lightweight solution for mobile and embedded devices. TensorFlow has always run on many platforms but as the adoption of ML models has grown exponentially over the last few years, so has the need to deploy them on mobile and embedded devices. TensorFlow Lite enables low-latency inference of on-device machine learning models.
Seamlessly deploy GenAI and ML models on billions of devices with Google's high-performance framework.
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- IT“Is this loss?”: A TensorFlow Lite app to detect Loss.jpg2018 · ▲82
I built this app over a week as a way to learn how to use TensorFlow with Mobilenets and to get some experience with Google Play (and partly as a dare). It's written in Java, as I wasn't able to find a Kotlin API for TFLite. It was built with Bazel. I'm pretty satisfied with the actual detector's performance, although I expect I could improve the UI a little bit. It's a weird UX, as you want it to be as simple and fast as possible (loss/notloss) but you also want it to have some sort of recognizability. I would love to hear your thoughts.…

Tensorflow Research Cloud2017 · sites.research.google · ▲288Accelerating open machine learning research with Cloud TPUs


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