Dog face detection with dlib - need advice on improving recal

问题

I'm trying to train a dog face detector with dlib's hog pyramid detector. I used Columbia dogs dataset: ftp://ftp.umiacs.umd.edu/pub/kanazawa/CU_Dogs.zip

At first I would get a recall of 0%, but by increasing C value I managed to increase it to 62% on training set and 53% on testing set. After certain point increasing C value stopped helping (1000+) and would only slow down training.

Precision is really high though, if it actually manages to find dog's face it's always correct, haven't seen any false positives.

Could you give any advice on how I could improve recall to a descent recall quality?

Thanks in advance

UPDATE: Following Davis King's advice, got the accuracy to 100% on training set and 80% on testing set just by training different detector per breed. I imagine it could be even higher if I cluster them by direction they're looking to.

回答1:

You probably need to train different detectors for different head poses and dogs that look very different. I would try running dlib's imglab command line tool with the --cluster option. That will cluster the images into coherent poses and you can train detectors for each pose.

来源：https://stackoverflow.com/questions/39398122/dog-face-detection-with-dlib-need-advice-on-improving-recal