<p><a href="http://www.cs.berkeley.edu/~rbg/" rel="nofollow">Dr Ross Girshik</a> has done a lot of work on object detection. You can learn a lot from his detailed git on <a href="https://github.com/rbgirshick/fast-rcnn" rel="nofollow">fast RCNN</a>: you should be able to find a caffe branch there, with a demo. I did not use it myself, but it seems very comprehensible.</p>

<p>Another direction you might find interesting is <a href="http://lsda.berkeleyvision.org/" rel="nofollow">LSDA</a>: using weak supervision to train object detection for many classes.</p>

<p>BTW, have you looked into <a href="http://arxiv.org/abs/1506.01497" rel="nofollow">faster-rcnn</a>?</p>
Trying to train the ImageNet model with Region CNN (R-CNN)
<p>After several month working with <a href="/questions/tagged/caffe" class="post-tag" title="show questions tagged &#39;caffe&#39;" rel="tag">caffe</a>, I've been able to train my own models successfully. For example further than my own models, I've been able to train ImageNet with 1000 classes.</p>

<p>In my project now, I'm trying to extract the region of my interest class. After that I've compiled and run the demo of <a href="https://github.com/rbgirshick/fast-rcnn" rel="nofollow"><strong>Fast R-CNN</strong></a> and it works ok, but the sample models contains only 20 classes and I'd like to have more classes, for example all of them.</p>

<p>I've already downloaded the <a href="http://image-net.org/download-bboxes" rel="nofollow"><strong>bounding boxes</strong></a> of ImageNet, with the real images.</p>

<p>Now, I've gone blank, I can't figure out the next steps and there's not a documentation of how to do it. The only thing I've found is how to train the INRIA person model, and they provide dataset + annotations + python script.</p>

<p>My questions are:</p>

<ul>
<li>Is there maybe any tutorial or guide that I've missed?</li>
<li>Is there already a model trained with 1000 classes able to classify images and extract the bounding boxes?</li>
</ul>

<p>Thank you very much in advance.</p>

<p>Regards.</p>

<p>Rafael.</p>