Which is faster when deploying cnn models by TensorFlow Lite, Caffe2 or OpenCV?

Question

We can deploy MobileNet on Smartphone by TensorFlow Lite, Caffe2 or OpenCV, and I think Caffe2 will provide the best performance with higher fps. But why? Is the performance gap between them so large? Thanks.

Answer 1

Each framework introduces their own optimizations, the result may be significantly different for different devices.

Answer 2

You should probably go for TensorFlow Lite. Last I looked, Caffe2 had almost zero smartphone GPU support, while TFLite now supports both iOS and many Android devices (all that have OpenGLES >=3.1). Using the GPU generally makes things several times faster, and you can reduce the inference precision to half-float (FP16) with TFLite for even more speed and not too much of a performance hit.

When you can't use the mobile GPU, you'll probably want to quantize your network to int8, which is easily doable with TensorFlow and TensorFlow Lite, whether during or after training. Caffe2 seems to need QNNPACK for quantization, which is claimed to be as much as 2 times faster. The catch is that it only works with two pre-trained models that they released (https://github.com/pytorch/QNNPACK/issues/12), so you can't convert your own model.

So I can't really think of a reason to use Caffe2 over TFLite.

I'm not sure about OpenCV's DNN module, but I seriously doubt it has mobile GPU support. There's a slight chance it has quantization.