onboard-coop/fast-methane-filters-models
By onboard-coop · ↓ 0 · ♥ 1 · Updated 2025-07-07
What's in this repo
This page lists the files in onboard-coop/fast-methane-filters-models, including final_checkpoint_model_40ep.onnx and more.
- 35 ONNX models (.onnx files)
- 213 files, including 105 .yaml, 35 .ckpt, 35 .onnx, 35 .json, 1 .md
- Last updated 2025-07-07
ONNX files
| File | Size | Download |
|---|---|---|
| LINKNET/CEM/LinkNet_RGB_CEM_A/final_checkpoint_model_40ep.onnx | — | Download |
| LINKNET/CEM/LinkNet_RGB_CEM_B/final_checkpoint_model_40ep.onnx | — | Download |
| LINKNET/CEM/LinkNet_RGB_CEM_C/final_checkpoint_model_40ep.onnx | — | Download |
| LINKNET/CEM/LinkNet_RGB_CEM_D/final_checkpoint_model_40ep.onnx | — | Download |
| LINKNET/CEM/LinkNet_RGB_CEM_E/final_checkpoint_model_40ep.onnx | — | Download |
| LINKNET/Mag1c-SAS/LinkNet_RGB_Mag1cSAS_A/final_checkpoint_model_40ep.onnx | — | Download |
| LINKNET/Mag1c-SAS/LinkNet_RGB_Mag1cSAS_B/final_checkpoint_model_40ep.onnx | — | Download |
| LINKNET/Mag1c-SAS/LinkNet_RGB_Mag1cSAS_C/final_checkpoint_model_40ep.onnx | — | Download |
| LINKNET/Mag1c-SAS/LinkNet_RGB_Mag1cSAS_D/final_checkpoint_model_40ep.onnx | — | Download |
| LINKNET/Mag1c-SAS/LinkNet_RGB_Mag1cSAS_E/final_checkpoint_model_40ep.onnx | — | Download |
Showing 10 of 35 .onnx files — see the full file list on the right.
How to use
These ONNX files can be loaded with ONNX Runtime. To inspect a model graph, upload the file to the Netron viewer. Download links point to the original files on Hugging Face.
README
Model Card
This Hugging Face repository contains models trained in the article "Optimizing Methane Detection On Board Satellites: Speed, Accuracy, and Low-Power Solutions for Resource-Constrained Hardware."
Model Overview
The models here were trained using the code available at the following GitHub repository:
- Training Code: HyperspectralViTs
The main project code, including filters benchmarking and demos, is available at the:
- Project Code: Methane Filters Benchmark
Data and Products
The precomputed products used for training were created by code in the main project repository:
- Products Creation Code: methane-filters-benchmark
Additionally, these precomputed products are hosted and accessible here:
- Dataset Repository: STARCOP-fast-products
Sample Usage
You can try out our models and demos directly in Google Colab using the provided notebooks:
-
Products Creation and Benchmarking Demo:
This notebook demonstrates generating products and measuring their runtime.
For local inference using the ONNX models, refer to the benchmark/onnx_inference_time.py script in the Project Code repository.
Citation
If you use these models in your research, please cite our article:
@misc{herec2025optimizingmethanedetectionboard,
title={Optimizing Methane Detection On Board Satellites: Speed, Accuracy, and Low-Power Solutions for Resource-Constrained Hardware},
author={Jonáš Herec and Vít Růžička and Rado Pitoňák},
year={2025},
eprint={2507.01472},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2507.01472},
}
Models performance
U-Net - CEM
| ID | Recall | Precision | F1 | F1 strong |
|---|---|---|---|---|
| A | 0.441 | 0.317 | 0.369 | 0.500 |
| B | 0.701 | 0.158 | 0.258 | 0.550 |
| C | 0.531 | 0.299 | 0.382 | 0.610 |
| D | 0.536 | 0.218 | 0.310 | 0.551 |
| E | 0.564 | 0.182 | 0.275 | 0.469 |
| AVG | 55.47% | 23.49% | 31.90% | 53.55% |
| STD | 8.41% | 6.30% | 4.94% | 4.85% |
U-Net - ACE
| ID | Recall | Precision | F1 | F1 strong |
|---|---|---|---|---|
| A | 0.468 | 0.202 | 0.282 | 0.460 |
| B | 0.480 | 0.288 | 0.360 | 0.537 |
| C | 0.413 | 0.253 | 0.314 | 0.461 |
| D | 0.550 | 0.194 | 0.287 | 0.510 |
| E | 0.500 | 0.162 | 0.245 | 0.442 |
| AVG | 48.22% | 21.99% | 29.77% | 48.19% |
| STD | 4.46% | 4.50% | 3.82% | 3.57% |
U-Net - MF
| ID | Recall | Precision | F1 | F1 strong |
|---|---|---|---|---|
| A | 0.603 | 0.153 | 0.243 | 0.451 |
| B | 0.673 | 0.198 | 0.306 | 0.585 |
| C | 0.563 | 0.259 | 0.355 | 0.507 |
| D | 0.625 | 0.173 | 0.271 | 0.558 |
| E | 0.466 | 0.301 | 0.366 | 0.496 |
| AVG | 58.60% | 21.68% | 30.82% | 51.94% |
| STD | 6.97% | 5.52% | 4.73% | 4.73% |
U-Net - MAG1C-SAS
| ID | Recall | Precision | F1 | F1 strong |
|---|---|---|---|---|
| A | 0.587 | 0.456 | 0.513 | 0.668 |
| B | 0.618 | 0.291 | 0.395 | 0.642 |
| C | 0.576 | 0.290 | 0.386 | 0.604 |
| D | 0.613 | 0.414 | 0.495 | 0.686 |
| E | 0.427 | 0.280 | 0.338 | 0.470 |
| AVG | 56.42% | 34.62% | 42.54% | 61.40% |
| STD | 7.04% | 7.38% | 6.73% | 7.71% |
U-Net - MAG1C (tile-wise)
| ID | Recall | Precision | F1 | F1 strong |
|---|---|---|---|---|
| A | 0.643 | 0.218 | 0.325 | 0.599 |
| B | 0.732 | 0.288 | 0.413 | 0.692 |
| C | 0.613 | 0.362 | 0.455 | 0.659 |
| D | 0.669 | 0.242 | 0.355 | 0.633 |
| E | 0.640 | 0.366 | 0.466 | 0.684 |
| AVG | 65.94% | 29.52% | 40.28% | 65.34% |
| STD | 4.04% | 6.05% | 5.51% | 3.42% |
LinkNet - CEM
| ID | Recall | Precision | F1 | F1 strong |
|---|---|---|---|---|
| A | 0.597 | 0.319 | 0.416 | 0.633 |
| B | 0.539 | 0.274 | 0.363 | 0.603 |
| C | 0.452 | 0.233 | 0.308 | 0.527 |
| D | 0.606 | 0.165 | 0.260 | 0.561 |
| E | 0.442 | 0.144 | 0.217 | 0.455 |
| AVG | 52.72% | 22.70% | 31.27% | 55.56% |
| STD | 6.96% | 6.54% | 7.09% | 6.20% |
LinkNet - MAG1C-SAS
| ID | Recall | Precision | F1 | F1 strong |
|---|---|---|---|---|
| A | 0.566 | 0.324 | 0.412 | 0.612 |
| B | 0.505 | 0.515 | 0.510 | 0.613 |
| C | 0.381 | 0.422 | 0.400 | 0.507 |
| D | 0.590 | 0.383 | 0.464 | 0.660 |
| E | 0.513 | 0.378 | 0.435 | 0.627 |
| AVG | 51.10% | 40.44% | 44.42% | 60.38% |
| STD | 7.24% | 6.35% | 3.95% | 5.14% |
Model info
Created: 2025-06-16
Updated: 2025-07-07
View on Hugging FaceFiles (213)
Related models
More from onboard-coop
No other models by this author.