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From what I understood, both from the paper and the code, the concept here is to train a neural network through triplets to produce a B-bit array binary output that will allow an effective yet fast search image retrieval search. More so, I assume that when you state "compact binary codes" you are mentioning to b and, implicitly, to the "codes" of each image has in the code of this project.
However, when you try 8 bits, 16 bits, 32 bits on your benchmark, which of the args are you changing on the training script? the subspace? or the product of the subspace and the subcenter? I say this because in your code a default value is
From what I understood, both from the paper and the code, the concept here is to train a neural network through triplets to produce a B-bit array binary output that will allow an effective yet fast search image retrieval search. More so, I assume that when you state "compact binary codes" you are mentioning to b and, implicitly, to the "codes" of each image has in the code of this project.
However, when you try 8 bits, 16 bits, 32 bits on your benchmark, which of the args are you changing on the training script? the subspace? or the product of the subspace and the subcenter? I say this because in your code a default value is
parser.add_argument('--subspace', default=4, type=int)
parser.add_argument('--subcenter', default=256, type=int)
In this case, is it 1024 bits?
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