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feymanpriv / DELG

Pytorch Implementation of Unifying Deep Local and Global Features for Image Search (DELG)

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Reranking process is too slow.

MurrayC7 opened this issue · comments

commented

Hi, @feymanpriv.

  • How long do you cost to test on the benchmark datasets (e.g., oxford5k)?
    I found that testing on these 5000 images costs over 0.6s per image for extracting features on a single GPU. Then it sounds incredible that reranking with local features costs over 200sec per query on a single process. Compared to the original tf implementation, it is too slow.

  • The used reranking parameters are default as NUM_RERANK = 100, MAX_RANSAC_ITERATIONS = 1000.

  • I am confused about whether I run in an improper way. Also, I have tried batch-wise running but failed due to the different sizes of each sample. Could you please help me accelerate it?