<li>A training dataset where objects appear only on a perfectly white background with nothing else present, but testing images where objects appear in a normal environment like living rooms or in natural scenes.</li>
</ul>
</p>
<p>
Another way you can mess this up is when using the <a
href="imaging.html#random_cropper">random_cropper</a> to jitter your training data, which is
common when training a CNN or other deep model. In general, the random_cropper finds images
that are more or less centered on your objects of interest and it also scales the images so
the object has some user specified minimum size. That's all fine. But what can happen is
you train a model that gets 0 training error but when you go and use it it doesn't detect
any objects. Why is that? It's probably because all the objects in your normal images, the
ones you give to the random_cropper, are really small. Smaller than the min size you told
the cropper to make them. So now your testing images are really different from your training
images. Moreover, in general object detectors have some minimum size they scan and if
objects are smaller than that they will never be found. Another related issue is all your
uncropped images might show objects at the very border of the image. But the random_cropper
will center the objects in the crops, by padding with zeros if necessary. Again, <b>make your
testing images look like the training images</b>. Pad the edges of your images with zeros if
needed.
</p>
</li>
<li><h3>Using a HOG based detector but not understanding the limits of HOG templates</h3>
The <ahref="fhog_object_detector_ex.cpp.html">HOG detector</a> is very fast and generally easy to train. However, you
have to be aware that HOG detectors are essentially rigid templates that are scanned over an image. So a single HOG detector
