This is very interesting and timely to my work, I had been struggling with training a Mobilenet CNN for classification of human emotions ("in the wild"), and struggling to get the model to converge. I tried multiclass to binary models e.g. angry|not_angry etc. But couldn't get past the 60-70% accuracy range.
I switched to extracting features from Imagenet and trained an xgboost binary and boom...right out of the box am seeing ~88% accuracy.
Also the author's points about speed of training and flexibility is major plus for my work. Hope this helps others.
I switched to extracting features from Imagenet and trained an xgboost binary and boom...right out of the box am seeing ~88% accuracy.
Also the author's points about speed of training and flexibility is major plus for my work. Hope this helps others.