| --- |
| 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} |
| } |
| ``` |
|
|
