Computer vision based planogram compliance evaluation. Code for the master's thesis of Julius Laitala, University of Helsinki, 2021. The thesis is available at http://urn.fi/URN:NBN:fi:hulib-202106092585 .

Currently, the functions in cvpce are set up to run only on CUDA. Therefore, you'll need a NVidia card to run cvpce. We suggest using Conda to avoid CUDA installation pains. To create a Conda environment for cvpce, simply utilize the provided environment.yml:

conda env create -f environment.yml
conda activate cvpce

With the Conda environment set up and activated, cvpce can be installed with setuptools:

pip install .

If you wish to tweak cvpce a bit, the -e flag is your friend!

cvpce is a command line tool, and a bunch of usage instructions can be accessed with the --help option. Go ahead and

cvpce --help

after installing to explore the available commands!

Pre-trained model weights are available in the releases.

The following public datasets were used for training and testing cvpce:

• GLN training and product proposal generation testing: SKU-110K of Goldman et al. (2019)
• DIHE training: the 2019 version of GP-180 (Tonioni et al. 2017)
• Classification and product detection testing: the Grocery Products dataset of George et al. (2014) with annotations from GP-180 (the 2017 version)
• Planogram compliance testing: planograms from GP-180 (2017 version) with fixes from this gist