Just tried it out, only one attempt and got good results! Couple of ideas, you may already have considered: 1) Use a color-correction algo to help with tinted

Just tried it out, only one attempt and got good results! Couple of ideas, you may already have considered: 1) Use a color-correction algo to help with tinted hair. Then use something like MASK-RCNN to get pixels of hair and get dominate cluster of color. 2) If lighting/shadows/environment causes so much trouble maybe have user rotate/tilt their head in circle-motion (like Apple has users do to train FaceID) then these run ~30 images through your algos and average the results? Maybe have torch on during capture to make the data more homogenous. (Or do once with torch, once without and train two different networks for both types of captures.) 3) If user manually overrides hair color show them your algos next 5 best predictions as other color choices so you can more easily retrain if user selects one of these other predictions. Maybe have them manually adjust bounding polygon of hair detection and even adjust image color correction sliders to show "what my hair actually looks like" (so you can train color correction). Just some ideas. Cool product! Good luck!