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I want to extract features from pre trained ResNet model for over 2M data. Problem? Even with the average pooling applied on the last layer's result, it provides a feature vector of length 2048 which looks something like [0.2,0.4,0.5,0.01,0.003,0.09....], if I convert to float32 for 200K images, I need 1.5GB of memory. But I have a bigger problem at hand now with a very huge dataset. I need to have a good algorithm which can decrease the dimensionality so that I can use any of ANNOY, LSH, SCANN, HNSWLIB etc for recommendation.

there are a few techniques that know but not sure about which one do good in case of Image Embeddings. Can someone suggest me which one would be good for my use case?

Some of the algorithm defined are in this and this blog but none them tells which one is better for embeddings.

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  • $\begingroup$ one approach is to cluster the returned embeddings and use the centroids only as representative classes $\endgroup$ – Nikos M. Jan 16 at 17:11
  • $\begingroup$ Well that is what I was thinking just yesterday. Make k clusters out of N images. And then within the clusters, search the image similarity BUT!! again, to make clusters, I have to get the data at ONCE for training and then apply some clustering method on the vectors, which again creates the problem of dimensionality. $\endgroup$ – Deshwal Jan 16 at 18:13

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