Trying to train the ImageNet model with Region CNN (R-CNN)

Question

After several month working with caffe, 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.

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 Fast R-CNN 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.

I've already downloaded the bounding boxes of ImageNet, with the real images.

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.

My questions are:

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

Thank you very much in advance.

Regards.

Rafael.

Answer 1

Dr Ross Girshik has done a lot of work on object detection. You can learn a lot from his detailed git on fast RCNN: you should be able to find a caffe branch there, with a demo. I did not use it myself, but it seems very comprehensible.

Another direction you might find interesting is LSDA: using weak supervision to train object detection for many classes.

BTW, have you looked into faster-rcnn?