<p>You need to offer us more details. The answer to what CNN should I use? and do I have enough images for that? depends on several factors:</p>

<p>1- How many objects for 550 images? Each object is a class, if you have 550 images from 2 different objects that might be enough, but if you have 550 objects thats only 1 image per object, which is definitely not enough.</p>

<p>2- What is the size of your images? Does it change among them? The 550 images contain parts of the object or the whole object?</p>

<p>After knowing the answer to these questions you can select your CNNs architecture and your data augmentation strategy.</p>

<p>Structured receptive fields have shown better results for small datasets than the normal CNN. Here's a papers to it: <a href="https://arxiv.org/abs/1605.02971" rel="nofollow noreferrer">https://arxiv.org/abs/1605.02971</a></p>
Deep Convolutional Networks
<p>I would like to do some object detection where have two restrictions. </p>

<p>First one is that at the moment I don't have large number of images for training (at the moment are around 550 images). </p>

<p>Second, most likely I will not be able to see the whole object, there will be available only some part of the object that I try to detect. </p>

<p>My question is it good to try Deep Convolutional Networks
via Bayesian Optimization and Structured Prediction for this kind of situation?</p>

<p>I have this paper as a reference: 
<a href="http://web.eecs.umich.edu/~honglak/cvpr15-cnn-detection.pdf" rel="nofollow noreferrer">Deep Convolutional Networks via Bayesian Optimization and Structured Prediction</a>.</p>