Person_reID_baseline_pytorch
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Reranking consumes too much memory space
I want to apply reranking for the MSMT17 dataset but the algorithm consumes too much memory space. Is there a way to consume less memory by achieving similar results? I have limited resources available that why cannot afford to increase the specs. Any insight, solution, and suggestion to this problem will be appreciated.
Hi @FatimaZulfiqar You may try the half-precision data type float16。
Okay thank you will try that but will the results be the same if I replace float32 with float16? Or will there be any effect on the result?
@FatimaZulfiqar
The result will be decrease by ~1% according to our experience.