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Well, it depends on the neural network you want to use. At computer vision/image processing conferences I find that it is much more common for people to use pre
by versteegen 11y ago
Well, it depends on the neural network you want to use. At computer vision/image processing conferences I find that it is much more common for people to use preexisting DNNs and plug them into their systems rather than train their own (why would you waste weeks or months on that?). The good pretrained DNNs available today tend to be huge, requiring a large amount of video RAM. I've talked to people writing realtime image processing software (GPU acceleration required) intended for handheld devices which can't yet actually run on the device because they don't have a suitable DNN which will fit in VRAM.
- dharma1 11y agomxnet has some optimised models for mobile devices - http://mxnet.readthedocs.org/en/latest/tutorial/smart_device.html http://mxnet.readthedocs.org/en/latest/tutorial/smart_device...
- nl 11y ago3.5% top-5 error rate on ImageNet running on Android. Hard to beat that (unless you are Microsoft Research Asia). https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android https://github.com/tensorflow/tensorflow/tree/master/tensorf...