---
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](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}, 
}