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--- |
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license: apache-2.0 |
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tags: |
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- image |
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- segmentation |
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- space |
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pretty_name: 'SWiM: Spacecraft With Masks (Instance Segmentation)' |
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size_categories: |
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- 1K<n<1M |
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task_categories: |
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- image-segmentation |
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task_ids: |
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- instance-segmentation |
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annotations_creators: |
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- machine-generated |
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- expert-generated |
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--- |
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--- |
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# SWiM: Spacecraft With Masks |
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A large-scale instance segmentation dataset of nearly 64k annotated spacecraft images that was created using real spacecraft models, superimposed on a mixture of real and synthetic backgrounds generated using NASA's TTALOS pipeline. To mimic camera distortions and noise in real-world image acquisition, we also added different types of noise and distortion to the images. |
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## Dataset Summary |
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The dataset contains over 64,000 annotated images with instance masks for varied spacecraft. It's structured for YOLO and segmentation applications, and chunked to stay within Hugging Face's per-folder file limits. |
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## How to Use |
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### Directory Structure Note |
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Due to Hugging Face Hub's per-directory file limit (10,000 files), this dataset is chunked: each logical split (like `train/labels/`) is subdivided into folders (`000/`, `001/`, ...) containing no more than 5,000 files each. |
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**Example Structure:** |
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labels/ |
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├── 000/ |
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│ ├── img_0.png |
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│ └── ... |
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├── 001/ |
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└── ... |
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If you're using models/tools like **YOLO** or others that expect a **flat directory**, you may need to **merge these subfolders at load-time or during preprocessing**. |
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## Code and Data Generation Pipeline |
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All dataset generation scripts, preprocessing tools, and model training code are available on GitHub: |
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[GitHub Repository: https://github.com/RiceD2KLab/SWiM](https://github.com/RiceD2KLab/SWiM) |
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## Citation |
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If you use this dataset, please cite: |
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@misc{sam2025newdatasetperformancebenchmark, |
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title={A New Dataset and Performance Benchmark for Real-time Spacecraft Segmentation in Onboard Flight Computers}, |
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author={Jeffrey Joan Sam and Janhavi Sathe and Nikhil Chigali and Naman Gupta and Radhey Ruparel and Yicheng Jiang and Janmajay Singh and James W. Berck and Arko Barman}, |
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year={2025}, |
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eprint={2507.10775}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2507.10775}, |
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} |
