<p>Not sure what you mean by proper documentation. This is an implementation of the paper (<a href="https://arxiv.org/pdf/1504.08083.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1504.08083.pdf</a>). Looks like you are trying to generate ROI's. Can you look through the helper functions as documented at the site to parse what you might need:</p>

<p>To run the toy example, make sure that in PARAMETERS.py the datasetName is set to "grocery".</p>

<ul>
<li><p>Run <code>A1_GenerateInputROIs.py</code> to generate the input ROIs for training and testing.</p></li>
<li><p>Run <code>A2_RunCntk_py3.py</code> to train a Fast R-CNN model using the CNTK Python API and compute test results.</p></li>
</ul>

<p>The algo will work on several candidate regions and then generate outputs: one for the classes of objects and another one that generates the bounding boxes for the objects belonging to those classes. Please refer to the code for getting the details of the implementation. </p>
Object detection using CNTK
<p>I am very new to CNTK.
I wanted to train a set of images (to detect objects like alcohol glasses/bottles) using CNTK - ResNet/Fast-R CNN.</p>

<p>I am trying to follow below documentation from GitHub; However, it does not appear to be a straight forward procedure. <a href="https://github.com/Microsoft/CNTK/wiki/Object-Detection-using-Fast-R-CNN" rel="nofollow noreferrer">https://github.com/Microsoft/CNTK/wiki/Object-Detection-using-Fast-R-CNN</a></p>

<p>I cannot find proper documentation to generate ROI's for the images with different sizes and shapes. And how to create object labels based on the trained models?  Can someone point out to a proper documentation or training link using which I can work on the cntk model? Please see the attached image in which I was able to load a sample image with default ROI's in the script. How do I properly set the size and label the object in the image ?   Thanks in advance!</p>

<p><a href="https://i.stack.imgur.com/HDxzZ.jpg" rel="nofollow noreferrer">sample image loaded for training</a></p>