История

Sergey Karayev da715ea07a removed mention of getting_pretrained_models page and old paths		2014-09-04 03:59:14 +01:00
..
templates	[example] image classification web demo	2014-07-11 23:41:12 -07:00
app.py	removed mention of getting_pretrained_models page and old paths	2014-09-04 03:59:14 +01:00
exifutil.py	FIX web_demo upload was not processing grayscale correctly	2014-08-26 00:45:19 -07:00
readme.md	removed mention of getting_pretrained_models page and old paths	2014-09-04 03:59:14 +01:00
requirements.txt	[fix] adding requirements.txt for web_demo. Closes #704	2014-07-29 23:29:33 -07:00

readme.md

title	description	category	include_in_docs	priority
Web demo	Image classification demo running as a Flask web server.	example	true	10

Web Demo

Requirements

The demo server requires Python with some dependencies. To make sure you have the dependencies, please run pip install -r examples/web_demo/requirements.txt, and also make sure that you've compiled the Python Caffe interface and that it is on your PYTHONPATH (see installation instructions).

Make sure that you have obtained the Reference CaffeNet Model and the ImageNet Auxiliary Data:

./scripts/download_model_binary.py models/bvlc_reference_caffenet
./data/ilsvrc12/get_ilsvrc_aux.sh

NOTE: if you run into trouble, try re-downloading the auxiliary files.

Run

Running python examples/web_demo/app.py will bring up the demo server, accessible at http://0.0.0.0:5000. You can enable debug mode of the web server, or switch to a different port:

% python examples/web_demo/app.py -h
Usage: app.py [options]

Options:
  -h, --help            show this help message and exit
  -d, --debug           enable debug mode
  -p PORT, --port=PORT  which port to serve content on

How are the "maximally accurate" results generated?

In a nutshell: ImageNet predictions are made at the leaf nodes, but the organization of the project allows leaf nodes to be united via more general parent nodes, with 'entity' at the very top. To give "maximally accurate" results, we "back off" from maximally specific predictions to maintain a high accuracy. The bet_file that is loaded in the demo provides the graph structure and names of all relevant ImageNet nodes as well as measures of information gain between them. Please see the "Hedging your bets" paper from CVPR 2012 for further information.