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
@article{le2026limodenet,
title = {LIMODENet: Attention-Free Compact Encoders for Information-Preserving
Onboard Satellite Image Restoration},
author = {Le, Thanh-Dung and Ha, Vu Nguyen and
Nguyen, Ti Ti and Chatzinotas, Symeon},
journal = {arXiv preprint arXiv:2609.14690},
year = {2026}
}