[goberoi]

Yeah, Clarifai did well. I am keen to learn how well their custom model feature works. Per their FAQ[0], you only need supply 20-50 images per concept. That seems remarkable to me, given that a concept like 'cow' has ~1500 images on Imagenet[1]. Perhaps they are using some sort of transfer learning to facilitate this? I.e. using a pretrained model, and then only retraining the last few fully connected layers, or retraining parts of the entire network?

I am not a deep learning practitioner, but would be curious to know from experts how their custom model feature might work; and from any of their users on how well it actually does.

Tablas: haha, great description of your teacher. I do play, with enthusiasm, but poorly. For those in Seattle, there is an amazing teacher who teaches up on Cap Hill [2].

[0] http://help.clarifai.com/custom-training/custom-training-faq

[1] http://image-net.org/synset?wnid=n01887787

[2] http://www.acitseattle.org

[cmarschner]

It is not necessary to train things from scratch; you take the largest imagenet model available and fine tune it for the task. This way it reuses much of the lower layers the have seen lots of data.