Use Tensorflow Object Detection API to detect small objects in images : <p>I'd like to use the Tensorflow <a href="https://github.com/tensorflow/models/tree/master/research/object_detection" rel="noreferrer">Object Detection API</a> to identify objects in a series of webcam images.  The <a href="https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md#coco-trained-models-coco-models" rel="noreferrer">Faster RCNN models</a> pre-trained on the COCO dataset appear to be suitable, as they contain all the object categories I need.</p>

<p>However, I'd like to improve the performance of the model at identifying fairly small objects within each image.  If I understand correctly, I need to edit the anchor <code>scales</code> parameter in the <a href="https://github.com/tensorflow/models/blob/master/research/object_detection/samples/configs/faster_rcnn_inception_v2_coco.config#L23" rel="noreferrer">config file</a> to get the model to use smaller bounding boxes.</p>

<p>My questions are: </p>

<ul>
<li>Do I need to re-train the model on the entire COCO dataset after I adjust this parameter?  Or is there a way to change the model just for inference and avoid any re-training?</li>
<li>Are there any other tips/tricks to successfully identifying small objects, short of cropping the image into sections and running inference on each one separately?</li>
</ul>

<h3>Background info</h3>

<p>I'm currently feeding 1280x720 images to the model.  At around 200x150 pixels I'm finding it harder to detect objects.</p>
