<p dir="auto">When loading a model (tensorflow, darknet) to a myriad VPU, following happens as I understood:</p>
<ol dir="auto">
<li>OpenCV reads the model and converts it to the IE representation.</li>
<li>The model will be transferred to the VPU.</li>
<li>Ready to accept forward requests.</li>
</ol>
<p dir="auto">Is it possible to release the converted model from the RAM, because it is already loaded into the VPUs RAM? Or did I misunderstand the whole concept? Due to limited RAM on our execution nodes, we would like to reduce the memory overhead required for bigger yolo networks.</p>
<p dir="auto">Kind Regards<br>
Roland</p>
<h5 dir="auto">System information (version)</h5>

<ul dir="auto">
<li>OpenCV =&gt; 4.5.1</li>
<li>Operating System / Platform =&gt; Windows 64 Bit</li>
<li>Compiler =&gt; Visual Studio 2019</li>
</ul>
<h5 dir="auto">Detailed description</h5>
<p dir="auto">There are vertical artifacts in image after a gaussian filter of size greater than 14x14 on the borders of an image. The picture is also a little bit more gray. This happens only for 16-bit images. It affects canny edge filter as well (weird edges), since it uses gaussian filter. It started first in version 4.5.1. In the previous version 4.5.0 the artifacts are not there, it may be caused by this commit: <a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="754161553" data-permission-text="Title is private" data-url="https://github.com/opencv/opencv/issues/18983" data-hovercard-type="pull_request" data-hovercard-url="/opencv/opencv/pull/18983/hovercard" href="https://github.com/opencv/opencv/pull/18983">#18983</a>. In this picture the is a visible artifact only on the left side, but it can create artifacts on the other side as well. Here is image before and after the filter:</p>
<p dir="auto"><a target="_blank" rel="noopener noreferrer nofollow" href="https://user-images.githubusercontent.com/9462235/126774537-718222f0-9c18-4f29-854b-d2c08d50d53a.png"><img src="https://user-images.githubusercontent.com/9462235/126774537-718222f0-9c18-4f29-854b-d2c08d50d53a.png" alt="image" style="max-width: 100%;"></a><br>
<a target="_blank" rel="noopener noreferrer nofollow" href="https://user-images.githubusercontent.com/9462235/126774562-41b1fd5c-ca6b-4ee0-86c7-1bedb158f5e3.png"><img src="https://user-images.githubusercontent.com/9462235/126774562-41b1fd5c-ca6b-4ee0-86c7-1bedb158f5e3.png" alt="image" style="max-width: 100%;"></a></p>
<p dir="auto">Zipped files with examples:<br>
<a href="https://github.com/opencv/opencv/files/6868331/test.zip">test.zip</a></p>
<h5 dir="auto">Steps to reproduce</h5>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="//open image
auto inputFile = &quot;test.tif&quot;
cv::Mat image = cv::imread(inputFile, cv::IMREAD_ANYDEPTH);
EXPECT_EQ(image.depth(), 2);

//perform gauss filter
cv::Mat gaused;
GaussianBlur(image, gaused, Size(15, 15), 1.0);

//save image after gauss
auto outputFile = &quot;testAfterGauss.tif&quot;);
cv::imwrite(outputFile, gaused);"><pre class="notranslate"><code class="notranslate">//open image
auto inputFile = "test.tif"
cv::Mat image = cv::imread(inputFile, cv::IMREAD_ANYDEPTH);
EXPECT_EQ(image.depth(), 2);

//perform gauss filter
cv::Mat gaused;
GaussianBlur(image, gaused, Size(15, 15), 1.0);

//save image after gauss
auto outputFile = "testAfterGauss.tif");
cv::imwrite(outputFile, gaused);
</code></pre></div>