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PyTorch Ensembler
https://github.com/QuantScientist/Deep-Learning-Boot-Camp/tree/master/Kaggle-PyTorch
ONNX install for Mac
git clone --recursive https://github.com/onnx/onnx.git git submodule update --init
POC Stitching
https://thilinasameera.wordpress.com/2012/03/24/translation-invariant-image-registration-using-phase-correlation-panorama-imaging-on-matlab/
ConvNet의 인퍼런스 속도를 높이는 4가지 방법
Play with your model and training hyper parameters. You might be able to use a lighter model without a significant degradation in performance, for example, decrease the network’s depth, width, number of filters, floating point accuracy… etc. This and methods #2,4 will allow you to increase your batch size and increase your inference bandwidth. Explore network architectures that are optimized for ‘lighter’ hardware such as Squeeze Net . NVIDIA offers a network inference optimizer called TensorRT that is designed exactly for your need - optimize your network for deployment. This is a more novel solution to increase inference speed but it should work - a famous paper by Geoffrey Hinton allows you to distill the knowledge in the network and compress it into a smaller network. This will somewhat degrade your performance, but the paper showed that the accuracy penalty is not very significant compared to the savings in model complexity.
LPF HPF by Julia
julia> using ImageFiltering, TestImages julia> img = testimage( "mandrill" ); julia> imgg = imfilter(img, Kernel.gaussian( 3 )); julia> imgl = imfilter(img, Kernel.Laplacian());
Image registration by Julia
> using Images, TestImages > img = testimage ( " mandril_gray " ); > img2 = subpixelshift (img, 40.3 , 30.7 ); > dftReg (img2,ref = img,ufac = 10 ) 1 x3 Array{Float64, 2 } : 0.332617 - 40.3 - 30.7
Bayesian filtering in the frequency domain¶
https://github.com/AllenDowney/ProbablyOverthinkingIt/blob/master/convolution.ipynb