https://github.com/macaodha/geo_prior

Presence-Only Geographical Priors for Fine-Grained Image Classification - ICCV 2019
https://github.com/macaodha/geo_prior

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Presence-Only Geographical Priors for Fine-Grained Image Classification - ICCV 2019

• Host: GitHub
• URL: https://github.com/macaodha/geo_prior
• Owner: macaodha
• Created: 2019-08-16T22:58:33.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2023-07-22T13:46:52.000Z (about 1 year ago)
• Last Synced: 2024-07-10T10:57:17.651Z (3 months ago)
• Language: Python
• Size: 1.04 MB
• Stars: 28
• Watchers: 5
• Forks: 9
• Open Issues: 8
• Metadata Files:
  • Readme: readme.md

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README

        
# Presence-Only Geographical Priors for Fine-Grained Image Classification

Code for recreating the results in our ICCV 2019 [paper](https://arxiv.org/abs/1906.05272).  

`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](https://homepages.inf.ed.ac.uk/omacaod/projects/geopriors/index.html).



  



### 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}

}

```