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experiments measuring how convolutional neural networks perform a visual search task

Project description

DOI

visual-search-nets

Experiments to measure the behavior of deep neural networks performing a visual search task.

For some background and a summary of the results, please see this Jupyter notebook.

Installation

Experiments were run using Anaconda on Ubuntu 16.04. The following commands were used to create the environment:

tu@computi:~$ conda create -n searchnets python=3.6 numpy matplotlib imageio joblib tensorflow-gpu 
tu@computi:~$ source activate searchnets
tu@computi:~$ git clone https://github.com/NickleDave/visual-search-nets.git
tu@computi:~$ cd ./visual-search-nets
tu@computi:~$ pip install .

usage

Installing this package (by running pip install . in the source directory) makes it possible to run the experiments from the command line with the searchnets command, like so:

tu@computi:~$ searchnets train config.ini

The command-line interface accepts arguments with the syntax searchnets command config.ini,
where command is some command to run, and config.ini is the name of a configuration file with options that specify how the command will be executed.
For details on the commands, see this page in the docs. For details on the config.ini files, please see this other page.

Data

Data is deposited here: https://figshare.com/articles/visual-search-nets/7688840

Replicating experiments

The Makefile replicates the experiments.

tu@computi:~$ make all

Acknowledgements

  • Research funded by the Lifelong Learning Machines program, DARPA/Microsystems Technology Office, DARPA cooperative agreement HR0011-18-2-0019
  • David Nicholson was partially supported by the 2017 William K. and Katherine W. Estes Fund to F. Pestilli, R. Goldstone and L. Smith, Indiana University Bloomington.

Citation

Please cite the DOI for this code: DOI

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