The implementation of the (Generalized) Non-Orthogonal Inverted Multi-Index structure from the paper "Efficient Indexing of Billion-Scale Datasets of Deep Descriptors", CVPR 2016.
The code requires Yael library and OpenBlas library.
learn_GNOIMI.cpp contains the code of the codebooks learning.
search_GNOIMI.cpp contains the code of search.
Thanks for your great work.
The two files, ./coarse.fvecs and ./fine.fvecs are needed when learn_GNOIMI.cpp is run, but I don't find the description about these two files in http://sites.skoltech.ru/compvision/noimi/.
Are they the initialized files of coarse vector matrix S and fine vector matrix T which are generated by K-means?
Thanks again.
Did you re-train the googleNet for image retrieval? How did you compress the output of googlenet into 96 dimension vector?
the dot product of two vectors(p-sk and tj) is a float, and in the function computeOptimalAlphaSubset, using memset(alphaNumerators[threadId], 0, K * K * sizeof(float)).
In learn_GNOIMI the K = 256,but in search_GNOIMI the K = 16384; How can they are different?
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