ltdung/limodenet

Attention-Free Compact Encoders for Information-Preserving
Onboard Satellite Image Restoration

Paper (arXiv) · Code (GitHub)

LIMODENet is a compact (0.69M-parameter), softmax-/QKV-free convolutional backbone for restoring channel-degraded Earth-observation imagery, designed under the constraint that it must also run as a spiking network on neuromorphic accelerators (BrainChip Akida, Intel Loihi-2). We do not claim it is the best restorer available — two modern CNN restorers (NAFNet, Restormer) beat it on raw fidelity. What we show is that it is the best restorer that is verifiably deployable where the power budget actually is: it converts end-to-end to a spiking network with zero blocked operations, while the fidelity-winning competitors have 22–24 each.

Code: pip install git+https://github.com/ltdung/limodenet

import torch
from limodenet.recon import build_recon
from huggingface_hub import hf_hub_download

path = hf_hub_download("ltdung/limodenet", "limodenet-skip-ae-1db100q.pth")
model = build_recon("limodenet_skip")
model.load_state_dict(torch.load(path))
restored = model(degraded_images)   # (N, 3, 128, 128), values in [0, 1]

Checkpoints

File	Task	Params	Headline number
limodenet-nano-eurosat.pth	classification	0.39M	95.65 ± 0.23% top-1 (EuroSAT, scratch, 3-seed)
limodenet-small-eurosat.pth	classification	1.55M	96.93 ± 0.16%
limodenet-base-eurosat.pth	classification	2.73M	97.26 ± 0.17%
limodenet-tiny-eurosat-tinft.pth	classification	0.69M	98.26% (Tiny-ImageNet pretrained + fine-tuned)
limodenet-big-eurosat-tinft.pth	classification	5.80M	off-ladder legacy config, Tiny-ImageNet FT
limodenet-ae-1db100q.pth	restoration	0.77M	37.39 ± 0.14 dB PSNR, 1dB/100q DVB-S2X
limodenet-skip-ae-1db100q.pth	restoration	0.78M	38.07 ± 0.26 dB PSNR, same condition
limodenet-spiking-classifier-t8.pth	classification, spiking	0.69M	93.55 ± 0.54% top-1, T=8, ANN-init
limodenet-spiking-skip-recon-t8.pth	restoration, spiking	0.78M	35.95 ± 0.05 dB PSNR, T=8, zero blocked ops

All classification numbers are on EuroSAT (10-class RGB land-use). All restoration numbers are on 1 dB $E_s/N_0$, JPEG quality 100 DVB-S2X-degraded EuroSAT, three-seed mean ± std unless noted. seed 0 of each 3-seed run is released; see the paper for the full 3-seed statistics.

Loading

# Classifier
from limodenet.family import build
model = build("tiny", num_classes=10)
model.load_state_dict(torch.load("limodenet-tiny-eurosat-tinft.pth"))

# Reconstructor
from limodenet.recon import build_recon
model = build_recon("limodenet_skip")   # or "limodenet" for the plain AE
model.load_state_dict(torch.load("limodenet-skip-ae-1db100q.pth"))

# Spiking classifier (needs `pip install snntorch`)
from limodenet.snn import SpikingLIMODENet
model = SpikingLIMODENet(num_classes=10, T=8)
model.load_state_dict(torch.load("limodenet-spiking-classifier-t8.pth"))

# Spiking reconstructor
from limodenet.snn_recon import SpikingLIMODENetSkipRecon
model = SpikingLIMODENetSkipRecon(T=8)
model.load_state_dict(torch.load("limodenet-spiking-skip-recon-t8.pth"))

License

Weights: CC-BY-NC-4.0 (non-commercial). Code: MIT. See the GitHub repo's WEIGHTS_LICENSE.md for details, or contact the authors for a commercial license.

Citation

@inproceedings{le2027limodenet,
  title     = {LIMODENet: Attention-Free Compact Encoders for Information-Preserving Onboard Satellite Image Restoration},
  author    = {Le, Thanh Dung and and Ha, Vu Nguyen and
               Nguyen, Ti Ti and Chatzinotas, Symeon},
  year      = {2027},
  notes     = {Under review}
}