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Presence-Only Geographical Priors for Fine-Grained Image Classification - ICCV 2019
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gen_figs plot all train locs Aug 26, 2019
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demo.py
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readme.md

Presence-Only Geographical Priors for Fine-Grained Image Classification

Code for recreating the results in our ICCV 2019 paper.

demo.py is a simple demo script that either 1) takes location as input and returns a prediction for all the categories predicted to be present at that location or 2) generates a dense prediction for a category of interest.
geo_prior/ contains the main code for training and evaluating models.
gen_figs/ contains scripts to recreate the plots in the paper.
pre_process/ contains scripts for training image classifiers and saving features/predictions.
web_app/ contains code for running a web based visualization of the model predictions.

Example Predictions

For more results, data, and an interactive demo please consult our project website.

example_predictions

Reference

If you find our work useful in your research please consider citing our paper.

@inproceedings{geo_priors_iccv19,
  title     = {{Presence-Only Geographical Priors for Fine-Grained Image Classification}},
  author    = {Mac Aodha, Oisin and Cole, Elijah and Perona, Pietro},
  booktitle = {ICCV},
  year = {2019}
}
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