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ConeQuest

ConeQuest is the first expert-annotated publicly available dataset for cone segmentation across three different regions on Mars, along with metadata for each sample.

bm_1

<img width="1028" alt="Screenshot 2023-11-11 at 6 26 39 PM" src="https://github.com/kerner-lab/ConeQuest/assets/46327378/dd1e2f27-39e8-4e0f-b4ad-40b6be3e8338">

More details about ConeQuest are available in this paper.

Getting Started

Environment Setup

conda env create -f conequest_env.yml

Download ConeQuest from Zenodo

./download_data.sh

Training and Testing models

Utilize the arguments described in main.py to train and test models for various configurations. A few important arguments are explained below:

python main.py \
    --if_training \
    --training_type 1 \
    --training_data_list "Isidis Planitia" \
    --train_model DeepLab
python main.py \
    --if_testing \
    --training_type 1 \
    --training_data_list "Isidis Planitia, Hypanis" \
    --train_model DeepLab
    --eval_data Hypanis

License

ConeQuest has a Creative Commons Zero v1.0 Universal license.

Citation

If you use ConeQuest in your research, please use the following citation:

@InProceedings{Purohit_2024_WACV,
    author={Purohit, Mirali and Adler, Jacob and Kerner, Hannah},
    title={ConeQuest: A Benchmark for Cone Segmentation on Mars},
    booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month={January},
    year={2024},
    pages={6026-6035}
}

Contact Information

Please reach out to Mirali Purohit mpurohi3@asu.edu, if you have any queries or issues regarding ConeQuest.