<p>According to the paper <a href="https://arxiv.org/pdf/1606.00915.pdf" rel="nofollow noreferrer">Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation</a> (3.1 DeeplabV3+ as an encoder), output_stride simply means the ratio between image input size and feature map output size (before global pooling). So change output_stride will change the output result.</p>
<p>just copy form <a href="https://github.com/tensorflow/models/issues/4233" rel="nofollow noreferrer">link</a>.</p>
Smaller output stride and bigger atrous rates produces larger heatmaps
<p>I am using DeepLabv3+ and I am running some tests. For my first run I used an <code>output_stride=16</code> and <code>atrous_rates=[6, 12, 18]</code> and in the 2nd run I used <code>output_stride=8</code> and <code>atrous_rates=[12,24, 36]</code>. Then I used tensorboard to see the results and I could notice that the heatmaps look larger and one "unit" is 4x bigger than the run with <code>output_stride=16</code>. <a href="https://i.stack.imgur.com/iOXuB.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/iOXuB.png" alt="enter image description here"></a> </p>

<p><code>output_stride=16</code></p>

<p><a href="https://i.stack.imgur.com/tKbn5.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/tKbn5.png" alt="enter image description here"></a></p>

<p><code>output_stride=8</code>
I would like to know what is the reason behing this behaviour and the consequences on my mIOU metric.</p>

<p>regards</p>