Caffe evaluation with selective search

Image classification with simple object detection running as a Flask web server.

Note that you should use this only on pictures with maximal size around 1024 x 1024. It can take really long time for bigger pictures.

Requirements

First thing you should do is to run this script:

./prepare.sh --get-all

this script will prepare proper folder structure and it will clone BVLC/Caffe repo and both AlpacaDB & belltailjp selective search algorithms. Then it will download pretrained reference mode and auxiliary files with help of script included in BVLC/Caffe.

Next thing you should do is compiling the Python Caffe interface. How-to do that is described here: http://caffe.berkeleyvision.org/installation.html but it can differ a little between versions of OS.

The server requires Python2 with couple of dependencies. You can install them manually one by one or run this command:

pip2 install -r ./web-app/dependencies.txt

If allready have caffe with compiled Python interface make sure that /path-to-caffe/python/ is on your PYTHONPATH.

Reference data

You should find reference models at ./caffe/models and auxiliary data at ./caffe/data/ilsvrc12/ if you run preparation script. If you did not, you can do it by yourself by running two scripts that are included in caffe:

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

Or you can use your own pretrained models.

Running the server

Running python2 examples/web_demo/app.py will bring up the server, accessible at http://127.0.0.1:5000. 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

Usage of web interface should be self descriptive. If not you can contact me and I will try to include some usage.

License

This implementation is publicly available under the Apache License, Version 2.0. See LICENSE.txt for more details.

References

This work is based on BVLC/Caffe web_demo example, which was just for simple evaluation of caffe models not selective search.

[1] Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor: Caffe: Convolutional Architecture for Fast Feature Embedding
[2] https://github.com/belltailjp/selective_search_py
[3] https://github.com/AlpacaDB/selectivesearch