Python 3 Keras 2.0.9 (tested with tensorflow backend) sklearn 0.19.1 (for evaluation) h5py (if we want to store model weights)

Download data

Download the following .json data files from version 1.2 of Visual Genome dataset (https://visualgenome.org/api/v0/api_home.html).

• image_data.json
• relationships_v1_2.json

Notice that we do not require actual images for our setting but only coordinates and bounding boxes. Assume we store the two files above in ./visualgenome folder.

Pre-process the data

In the terminal, cd to the ./code folder of this repository. Run:

python pre-process_data.py

passing the right paths and desired choices (i.e., implicit, explicit, or all relations) as arguments. See --help for details.

Train and evaluate the model

Run on the terminal:

python learn_and_eval.py

passing the desired choices as arguments (e.g., PIX or REG model, etc.). See --help for details.

Results are automatically stored in the ./results folder.