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- don't need as much labeled localization data as you do labeled data for classification
- that is build a classifier with the large amount of training data that labels classes
- now use transfer learning to learn a less complicated model from the smaller amount of labeled localization data
Annotating
-maybe not as expensive as one thinks, options
- http://labelme.csail.mit.edu/
opensource tool for labeling, I labeled 250 xtcav images. You can only upload 20 at a time, this is tedious. You have to give each box a name, which takes unnecessary time for our problem. I'd estimate 8 seconds per box, some images have two boxes, some 1. - mechanical turk - the labelme website talks about using this: http://labelme2.csail.mit.edu/Release3.0/browserTools/php/mechanical_turk.php
in particular they report a price per image of only 1 penny? And I think those images are more complicated than ours
Good Results with Little Training Data
Here is what it looks like to work in labelme - note labelme only takes jpg to the image quality is poorer:
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Below we plot results. Overall, the predicted boxes, always in green, look surprisingly good. I only found one 'bad' one for each of e1, and e2, which I plot below.
e1 images
This is the one bad one - the 10th of about 13 test images
e2 images
images
