tfjs-yolo-tiny

One very practical reason is to save on computation costs. GPU servers are not cheap and if you're going to run on CPU anyway, you might as well run it locally on the user's browser. Some demos such as real time object detection isn't possible at all if you had to pay roundtrip server latency (not to mention the complexity of streaming video to and from your server): https://github.com/ModelDepot/tfjs-yolo-tiny And a lot of web demos such https://affinelayer.com/pixsrv/ I don't believe will be up for such a long time if the author had to pay ongoing server costs.

The most practical use not mentioned here is probably to import existing trained models/weights. I can see it being useful for anything that you want to run in real-time (e.g., webcams apps like https://github.com/ModelDepot/tfjs-yolo-tiny) and can't pay a round-trip cost to server. https://js.tensorflow.org/tutorials/import-keras.html Training a model in the browser is the least practical use for tensorflow.js IMO (unless maybe you want to hijack people's browsers to help with training or something).

Hey Everyone! After seeing the announcement of Tensorflow.js, and being a lover of both JS and ML, I couldn't help myself from creating a demo with a non-trivial ML model. YOLO is definitely one of the most interesting/useful ML models out there (as we saw on ModelDepot) and thought that'd be cool to get working. As far as I know, there hasn't been any prior work with getting YOLO object detection working in JS, I assume mainly because the post-processing code to transform the model's outputs to bounding boxes is a whole heap of math. Luckily tf.js makes it a lot easier to translate from Python and so here it is! You can drop this into your project with just a couple lines of code (and no ML required) using NPM! (via npm i tfjs-yolo-tiny) If you want to help with perf/bug fixes, PR's are more than welcome @ https://github.com/ModelDepot/