SPARK — training weights

Companion weights for the SPARK spacecraft-perception experiments. Each file supports one result or finding in the project.

Findings these support

• 2022 pretraining adds nothing when fine-tuning on 60k images (runA ≈ runB).
• Label quality was the biggest lever: fragmented → clean component labels lifted instance mask mAP50-95 from ≈0.31 to ≈0.60.
• Fractional box padding (δ = 0.02) raised mean box IoU from 0.884 to 0.937.

Usage

from ultralytics import YOLO
model = YOLO("modela-det.pt")   # or any file above

Data: SPARK 2022 / SPARK 2026 (CVI2, University of Luxembourg).

Citation

@dataset{rathinam_2022,
  author       = {Rathinam, Arunkumar and Gaudilliere, Vincent and
                  Mohamed Ali, Mohamed Adel and Ortiz Del Castillo, Miguel and
                  Pauly, Leo and Aouada, Djamila},
  title        = {SPARK 2022 Dataset : Spacecraft Detection and Trajectory Estimation},
  month        = jun,
  year         = 2022,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.6599762},
  url          = {https://doi.org/10.5281/zenodo.6599762}
}

@dataset{rathinam_2024_10908215,
  author       = {Rathinam, Arunkumar and Mohamed Ali, Mohamed Adel and
                  Gaudilliere, Vincent and Aouada, Djamila},
  title        = {SPARK 2024: Datasets for Spacecraft Semantic Segmentation
                  and Spacecraft Trajectory Estimation},
  month        = feb,
  year         = 2024,
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.10908215},
  url          = {https://doi.org/10.5281/zenodo.10908215}
}