This repo contains pretrained models that predict relative upvotes on Reddit for image-only and image + text models. The subreddits these models were trained on are /r/pics, /r/aww, /r/cats, /r/FoodPorn, /r/MakeupAddiction, and /r/RedditLaqueristas, so if you want to know if an image would probably be upvoted within these communities, you've come to the right place! If you want to read more about the technical details, check out the project page and paper here.
To install requirements, run
pip install -r requirements.txt
If you want to score according to the /r/aww community
python score_example.py examples/bodhi.jpg aww
which outputs:
examples/bodhi.jpg 34.8/100
the first column is the filename, and the second column is the score out of 100 for the image (higher is better). The score is the percentile of the image's score on a test split.
If you want to score a cat alongside a caption according to the /r/cats community, you can do
python score_example.py examples/taz.jpg cats --caption "Please don't sit on me!"
which outputs
examples/taz.jpg please dont sit on me 55.8/100
If you want to score many images/captions at once, you can use
--list_mode True; in this case, the image and caption arguments are
assumed to be text files. The image text file has one filename per
line, and the caption text file has one caption per line. The first
line of the image file should correspond to the first line of the
caption file, and so on. For example, you can run
python score_example.py examples/example_image_list.txt --caption examples/example_caption_list.txt cats --list_mode True
which outputs
examples/bodhi.jpg who says bulldogs cant be c... 22.1/100
examples/lizzy.jpg my 20 year old little girl ... 99.4/100
examples/taz.jpg please dont sit on me 55.8/100
Unsurprisingly, the model doesn't like a dog (Bodhi) being posted in /r/cats, though the model likes the story about an elderly cat (Lizzy). As an interesting experiment, you can check the effect the captions had on the scores by running
python score_example.py examples/example_image_list.txt cats --list_mode True
and comparing to the previous output.
If you want to train your own models, you'll need to get the datasets that these were trained on, which are not in this repo. They are available for download here.
If you find the models here useful, please cite our paper!
@inproceedings{hessel2017cats,
title={Cats and Captions vs. Creators and the Clock: Comparing Multimodal Content to Context in Predicting Relative Popularity},
author={Hessel, Jack and Lee, Lillian and Mimno, David},
booktitle={Proceedings of the 26th International Conference on the World Wide Web},
year={2017},
organization={International World Wide Web Conferences Steering Committee}
}
If you have any questions, you can contact jhessel@cs.cornell.edu