How to calculate receptive field size?
<p>I'm reading paper about using CNN(Convolutional neural network) for object detection.</p>

<p><a href="https://www.cs.berkeley.edu/~rbg/papers/r-cnn-cvpr.pdf" rel="noreferrer">Rich feature hierarchies for accurate object detection and semantic segmentation</a></p>

<p>Here is a quote about receptive field:</p>

<pre><code>The pool5 feature map is 6x6x256 = 9216 dimensional. Ignoring boundary effects, each pool5 unit has a receptive field of 195x195 pixels in the original 227x227 pixel input. A central pool5 unit has a nearly global view,
while one near the edge has a smaller, clipped support.
</code></pre>

<p>My questions are:</p>

<ol>
<li>What is definition of receptive field?</li>
<li>How they compute size and location of receptive field?</li>
<li>How we can compute bounding rect of receptive field using caffe/pycaffe? </li>
</ol>