Great feedback, I really appreciate you taking time to read the post. Though I would like to make a few notes regarding what we are trying to achieve and some improvements we've made:
1) Our target audience is someone who hasn't taken Deep Learning 101 but wants to solve a problem
2) We are focusing on users who don't want to setup their own deep learning machines and don't want to learn how to use tensorflow/kerras/caffe/theano and spend time maintaining their own boxes, they don't want to spend engineering effort in ensuring slas and uptime along with scalability
3) We have made improvements in both the model we use for our product and the way it is retrained. It's not the same as the tensorflow example
4)The model has a different dataset than ImageNet and provides additional value in being better suited to certain tasks.
Worst case we have something nobody wants and that's valuable insight in itself. In the best case we have made something that people learn in Deep Learning 101 that can now be used by anybody without spending time and get straight to solving problems.
