<p>Quoting <a href="https://arxiv.org/pdf/1506.01497.pdf" rel="nofollow noreferrer">faster-RCNN paper</a>:</p>

<blockquote>
  <p>An  important  property  of  our  approach  is  that  it is
  translation invariant, both  in  terms  of  the  anchors and  the 
  functions  that  compute proposals  relative  to the  anchors.  If 
  one  translates  an  object  in an image, the  proposal  should 
  translate  and  the  same  function should  be  able  to  predict  the
  proposal  in  either  location.  This  translation-invariant  property
  is  guaranteed by our method*</p>
  
  <p>*As is the case of FCNs [7], our network is translation invariant up to the network’s total stride</p>
</blockquote>

<p>So the short answer is that you'll probably be ok with the object is mostly at a certain location in the train set and somewhere else in the test set.  </p>

<p>A bit longer answer is that the location may have side affects that may affect the accuracy and it will probably be better to have the object in different locations; however you can try to add - for testing purposes - N test samples to the train set and see what is the accuracy change in the test set -N remaining samples.</p>
Is the object location in train effect the results for Faster RCNN?
<p>Has enyone try the effect of the location per class in faster rcnn?</p>

<p>In case my train data has one of the object classes always in one area of the frame, lets say in the top right of the image,  and on the evaluation dataset I have one image that this object is on other area, down left,
Is the Faster RCNN capable to handle with this case? </p>

<p>Or if I want my network to find all of the classes in all of the frame areas I need to provide example in the train dataset that cover all the areas? </p>