[example] finetuning CaffeNet on Flickr Style data

This example shows how to fine-tune the BVLC-distributed CaffeNet model on a different dataset: [Flickr Style](http://sergeykarayev.com/files/1311.3715v3.pdf), which has style category labels.
The dataset is distributed as a list of URLs with corresponding labels.
Using a script, we will download a small subset of the data, split it into train and val portinos, and use the ImageDataLayer to load images directly from the filesystem.
"On only 200 instances..." or the like. One strong point of fine-tuning is needing less data than the infamous voracity of deep learning from scratch.