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Comment by RubenSandwich | Hacker News Reader

Apple uses a different model format[0], so tensorflow models need to be converted first before they can be used. So Apple would need to add support for this tensorflow lite/mobile model format to work in Core ML.

[0]https://developer.apple.com/documentation/coreml/converting_...

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Another format is onnx [0], where Apple and Goodle don't seem to participate. I don't know the politics behind it but there should be a common format for all libraries/platforms.

[0] https://onnx.ai/

onnx seems to be an initiative to try to allow Microsoft's and Facebook's AI platforms to compete with Tensorflow.

Considering Tensorflow is more a grab at developer mindshare than an ideal platform [for example, its performance lags by a factor of two behind MXNet and Torch], I think it's a smart plan.

I don't think the last sentence is fair to trensorflow. Torch has been around for ~15 years compared to the 3 of TF. You'd expect TF to catch up in terms of performance in the future.

I think it's more than fair. I'm certain that more developer-hours have been spent on TensorFlow than on Torch.

They very well may catch up, but that doesn't mean that hype and the Google name aren't the reason it's gotten popular.

Question: So does this mean, Tensorflow Lite will have worse performance on iOS than a natively implemented CoreML model of the same design would?

Not necessarily. They are just data formats that encode the different weights and layers of the neurel net. (That is an oversimplification, but enough to give you a basic grasp of what is happening under the covers.) Apple is obviously investing a lot into CoreML, so expect some speed ups just due to effort but I would not expect factors of magnituite difference in performance. (This might be wrong as Tensorflow Lite is still quite immature, but as it matures I believe it will be comparable in performance.)

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