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I am wondering why tf object detection api needs so few picture samples for training while regular cnns needs many more?

What I read in tutorials is that tf object detection api needs around 100-500 pictures per class for training (is it true?) while regular CNNs need many many more samples, like tens of thousands or more. Why is it so?

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    $\begingroup$ I haven't work with the tf object detection api so I might be totally wrong but I guess that they need so little data because their models are already trained on huge datasets, and they are just transferring the learning. $\endgroup$ – razvanc92 Aug 22 at 14:09
  • $\begingroup$ You actually might be right :) haven't thought about it, thanks! $\endgroup$ – Makintosz Aug 22 at 14:17
  • $\begingroup$ you are right. Actually from scratch OD needs more images than Classifiers because its a more difficult task $\endgroup$ – mshlis Aug 22 at 15:26
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    $\begingroup$ @razvanc92 you can put your comment as an answer for this question :) $\endgroup$ – malioboro Aug 23 at 8:54
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I guess that they need so little data because their models are already trained on huge datasets, and they are just transferring the learning (using those pre-trained models as starting point).

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