ConeQuest is the first expert-annotated publicly available dataset for cone segmentation across three different regions on Mars, along with metadata for each sample.
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ConeQuest can be accessed from Zenodo.
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The ConeQuest dataset has a total of 13,686 patches from 8 different subtiles across 3 regions.
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Metadata of each CTX tile across three regions used in the creation of ConeQuest:
![Screenshot 2023-11-11 at 6 26 39 PM](https://private-user-images.githubusercontent.com/46327378/282272872-dd1e2f27-39e8-4e0f-b4ad-40b6be3e8338.png?jwt=eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.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.TfCaGJfq6HUI5mONvWj6FYm_YkZ5T-jpzxi8xhKOCfY)
More details about ConeQuest are available in this paper.
conda env create -f conequest_env.yml
./download_data.sh
Utilize the arguments described in main.py to train and test models for various configurations. A few important arguments are explained below:
- Provide training_type argument 1 or 2 to train a model for benchmarks 1 and 2, respectively.
- In the training_data_list, provide a list (string separated with a comma) of region/s or size/s on which model will be trained, e.g., "Isidis Planitia, Hypanis" or "small, medium".
- Use if_training_positives to train your model only on positive samples.
python main.py \
--if_training \
--training_type 1 \
--training_data_list "Isidis Planitia" \
--train_model DeepLab
- In the eval_data, provide which region or size to evaluate.
python main.py \
--if_testing \
--training_type 1 \
--training_data_list "Isidis Planitia, Hypanis" \
--train_model DeepLab
--eval_data Hypanis
ConeQuest has a Creative Commons Zero v1.0 Universal license.
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}
}