This repository contains code for running experiments from the following paper:

Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks.
Eric Crawford and Joelle Pineau.
AAAI (2019).

@inproceedings{crawford2019spatially,  
  title={Spatiial Invariant Unsupervised Object Detection with Convolutional Neural Networks},  
  author={Crawford, Eric and Pineau, Joelle},  
  booktitle={Thirty-Third AAAI Conference on Artificial Intelligence},  
  year={2019}
}

This repository and the companion repository dps are both likely to undergo further development in the future. In general, we will attempt to keep the experiments from the paper runnable, but in case something breaks, one can always check out the aaai_2019 branches of both repositories to obtain the code as it was for the paper. Also, branches for both repos named aaai_2019_v1 preserve most of the behaviour of the original experiments, but with significant code improvements.

Installation

1. Install tensorflow with GPU support. auto_yolo was developed with tensorflow 1.13.2 and CUDA 10.0; we cannot guarantee that it will work with other versions. In particular, versions of tensorflow >= 1.14 introduce changes to the process of building custom tensorflow ops (of which this repo has 2) which this repo has not yet adapted to.

2. sh install.sh

3. Install a version of tensorflow_probability that matches your version of tensorflow (0.6 works for tensorflow 1.13, increment by 0.1 for each 0.1 increment of tf version).

Running Experiments

To train SPAIR on a scattered MNIST dataset:

cd auto_yolo/experiments/comparison
python yolo_air_run.py local