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Repository for accessing and training using GeoNet dataset, published at CVPR 2023.

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GeoNet - CVPR 2023

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Project Page | Arxiv | Project Video | Challenge

This repo contains the dataloader and training scripts for GeoNet dataset released as part of our GeoNet paper in CVPR2023. The repository also contains baseline adaptation methods towards GeoNet challenge in ICCV2023.

GeoNet Dataset and Statistics

GeoPlaces GeoImnet
USA-Train 178110 154908
USA-Val 17234 16784
Asia-Train 187426 68722
Asia-Val 26923 9636

Our GeoNet dataset is the largest yet for geographical domain adaptation, covering image classification as well as scene classification tasks. Additionally, the challenge also involves universal domain adaptation, please see the challenge website for the details.

Data Download.

The dataset is currently hosted on google drive, and can be downloaded using the following links.

Metadata JSON

The metadata contains additional information along with the images such as captions, tags and geolocations. The metadata is currently organized as a JSON file, and can be downloaded along with the images.

Images Metadata
GeoPlaces Link Link
GeoImnet Link Link
GeoUniDA Link Link

Training

The model training can be performed by appropriately changing the configuration file in config/ based on the task and dataset. For example, to run USA -> Asia on GeoPlaces, run

python3 train.py --config configs/plain.yml --source usa --target asia --num_class 205 --data_root /data/GeoNet/ --json_dir /data/geoPlaces_metadata.json --num_iter 100000 --exp_name plain --trainer plain

Our codebase currently supports plain source-only training and CDAN training, we will add more methods in the future.

Citation

If you use our dataset, please cite us using

@inproceedings{kalluri2023geonet,
  title={GeoNet: Benchmarking Unsupervised Adaptation across Geographies},
  author={Kalluri, Tarun and Xu, Wangdong and Chandraker, Manmohan},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={15368--15379},
  year={2023}
}

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Repository for accessing and training using GeoNet dataset, published at CVPR 2023.

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