File size: 2,854 Bytes
f48664a 2d5d49b 8a523b6 2d5d49b f48664a 2d5d49b 8a523b6 2d5d49b 8a523b6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 | ---
language: en
license: cc-by-nc-sa-4.0
library_name: pytorch
pipeline_tag: image-to-image
tags:
- diffusion
- image-restoration
- remote-sensing
- underwater
---
# KAUSTics-Net: selected result checkpoints
Pixel-space conditional velocity-diffusion model for through-water UAV restoration. This model has no latent autoencoder. Use the accompanying KAUSTics-Net GitHub code; these files are not Diffusers pipeline checkpoints.
## Contents
| Directory | Seed checkpoints | Initialization / selected epoch |
|---|---|---|
| `kaustics/` | `seed1.pt`, `seed2.pt`, `seed3.pt` | Scratch KAUSTics training; epochs975/925/925 |
| `sea_undistort/` | `seed1.pt`, `seed2.pt`, `seed3.pt` | Corresponding KAUSTics seed, sampler-aware fine-tuning; epochs95/100/100 |
Only the six selected model weights are included. No optimizer states, intermediate epochs, pilots, ablations or competitor weights. `selected.json` records SHA256 checksums, original archival checkpoint checksums and unchanged tensor digests. The lightweight release exports CPU tensors plus primitive metadata; load with `torch.load(..., map_location='cpu', weights_only=True)`.
## Inference
Use `infer.py --checkpoint checkpoints/kaustics/seed1.pt --input image.png --output predictions/example` in the code release. Input/output256×256 RGB; input normalization[-1,1];1000-step cosine schedule;25-step DDIM at strength0.95 and classifier-free guidance2.0. Preserve image filenames for deterministic per-image noise.
KAUSTics training uses source-disjoint26/3/3 UAV source IDs. Sea-Undistort fine-tuning uses840 all-effects training scenes and240 all-effects validation scenes; all three120-scene test variants are scored against original GT. See the code release’s `docs/PROTOCOL.md` for loss weights, selection, stopping rules, benchmark exposure and portability limits.
These weights target the paper’s dataset/task. Restoration quality and geometric accuracy on unrelated real UAV scenes are not guaranteed. Sea-Undistort adaptation shows a fidelity/perceptual trade-off; it is not a claim of universally improved image quality.
## License
The model weights are licensed under [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/).
## Citation
If you use this release in your research, please cite our [arXiv preprint](https://arxiv.org/abs/2611.03120).
```bibtex
@article{scilla2026kausticsnet,
title = {{KAUSTics-Net}: Diffusion-Based Through-Water {UAV} Restoration with a Semi-Synthetic Dataset},
author = {Scilla, Dario and Lopez, Omar A. and Johansen, Kasper and Nieuwenhuis, Brian O. and McCabe, Matthew F.},
journal = {arXiv preprint arXiv:2611.03120},
year = {2026},
doi = {10.48550/arXiv.2611.03120},
url = {https://arxiv.org/abs/2611.03120}
}
```
|