| | --- |
| | license: apache-2.0 |
| | tags: |
| | - image |
| | - segmentation |
| | - space |
| | pretty_name: 'SWiM: Spacecraft With Masks (Instance Segmentation)' |
| | size_categories: |
| | - 1K<n<1M |
| | task_categories: |
| | - image-segmentation |
| | task_ids: |
| | - instance-segmentation |
| | annotations_creators: |
| | - machine-generated |
| | - expert-generated |
| | --- |
| | |
| | --- |
| |
|
| | # SWiM: Spacecraft With Masks |
| |
|
| | 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. |
| |
|
| | ## Dataset Summary |
| | The dataset contains over 63,917 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. |
| |
|
| |
|
| | ## How to Use |
| | ### Directory Structure Note |
| |
|
| | 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. |
| |
|
| | **Example Structure:** |
| |
|
| | labels/ |
| | ├── 000/ |
| | │ ├── img_0.png |
| | │ └── ... |
| | ├── 001/ |
| | └── ... |
| | |
| | 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**. |
| | |
| | ## Utility Scripts |
| |
|
| | ### 1. Setup |
| |
|
| | Create your virtual environment: |
| |
|
| | python -m venv env |
| | |
| | source env/bin/activate # On Windows: env\Scripts\activate |
| | |
| | pip install -r requirements.txt |
| |
|
| |
|
| | ### 2. Sample 500 items from a specific chunk: |
| |
|
| | python sample_swim.py |
| | |
| | --repo-id JeffreyJsam/SWiM-SpacecraftWithMasks |
| | |
| | --image-subdir Baseline/images/val/000 |
| | |
| | --label-subdir Baseline/labels/val/000 |
| | |
| | --output-dir ./Sampled-SWiM |
| | |
| | --count 500 |
| | |
| | ### 3. Download the entire dataset (optionally flatten chunks): |
| | |
| | python download_swim.py |
| | |
| | --repo-id JeffreyJsam/SWiM-SpacecraftWithMasks |
| | |
| | --images-parent Baseline/images/val |
| | |
| | --labels-parent Baseline/labels/val |
| | |
| | --output-dir ./SWiM |
| | |
| | --flatten |
| |
|
| | |
| | **Arguments are all configurable—see `--help` for details.** |
| |
|
| | ## Code and Data Generation Pipeline |
| |
|
| | All dataset generation scripts, preprocessing tools, and model training code are available on GitHub: |
| |
|
| | [GitHub Repository: https://github.com/RiceD2KLab/SWiM](https://github.com/RiceD2KLab/SWiM) |
| |
|
| |
|
| | ## Citation |
| |
|
| | If you use this dataset, please cite: |
| |
|
| | @misc{sam2025newdatasetperformancebenchmark, |
| | title={A New Dataset and Performance Benchmark for Real-time Spacecraft Segmentation in Onboard Flight Computers}, |
| | 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}, |
| | year={2025}, |
| | eprint={2507.10775}, |
| | archivePrefix={arXiv}, |
| | primaryClass={cs.CV}, |
| | url={https://arxiv.org/abs/2507.10775}, |
| | } |
