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I have a dataset consist of 260 thousands images that are extracted from several videos. I want to extract features of these images and use them for frame retrieval. I used VGG16 (pretrained on imagenet) that implemented in Keras library with 'avg' pooling in the last convolutional layer. VGG16 gives me a vector consist of 512 number (feature) for each image. The only reason that bothers me is that this scenario is too time-consuming. For my dataset, it took about a day and 6 hours which is too much.
Is this elapsed time normal?
Because of low performance, I switch from VGG16 to DenseNet121 that already implemented in Keras. For this model (now) it took a day and 18 hours to extract features from 33% of my images (about 86000).
I ask again: Is this elapsed time normal?
Is there any way to extract feature faster? Even without using of implemented algorithms?
If you need more clarification, just ask for it. Thank You!

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  • $\begingroup$ The speed depends on your hardware. Are you using a good gpu? $\endgroup$
    – Alex R.
    Sep 14, 2019 at 20:46
  • $\begingroup$ Nah, I'm using CPU :| I have Nvidia 740 GT, is it a good GPU? absolutely not :/ but can I use it anyway? $\endgroup$ Sep 15, 2019 at 3:22

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