Datasets:
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 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.
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.
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
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}, }