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aakanshajagga/lunarsync-hybrid-v2
lunarsync-hybrid-v2 is a machine learning model from aakanshajagga. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.
Shipping, cloud-trained correspondence pipeline for seven overlapping Chandrayaan-2 OHRC acquisitions.
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From the Hugging Face model README
Shipping, cloud-trained correspondence pipeline for seven overlapping Chandrayaan-2 OHRC acquisitions.
| Metric | Result |
|---|---|
| Five-fold held-out controls | 342,408 |
| Median error | 0.412 px |
| p95 error | 0.770 px |
| PCK@1 px | 99.812% |
| PCK@2 px | 100.000% |
| Held-out acquisition PCK@1 | 99.776% |
| Visual gate test AUROC | 0.962 |
| Visual gate false-accept rate | 0.75% |
| 10k-point CPU latency | 114.0 ms |
The degree-9 geometry maps are refit independently in every spatial fold. Visual train, validation and test acquisitions are product-disjoint.
metadata_geometry (production): deterministic pixel→lunar-ground→pixel correspondence.visual_gate (production safety): applies a local visual shift only above the validation-calibrated threshold; otherwise retains geometry.evaluation_report_v2.json.from huggingface_hub import snapshot_download
from inference import LunarSyncShippingPipeline
root=snapshot_download('aakanshajagga/lunarsync-hybrid-v2')
pipe=LunarSyncShippingPipeline(root)
result=pipe.correspond(source_product,target_product,[[1000,1000]])
This release supports the product IDs in geoprior_products.json. It requires PDS geometry metadata and does not generalize to arbitrary lunar imagery or TMC-2 without new calibrated data.
Open the browser-side Hugging Face Static Space. Geometry inference and the safety gate run client-side; no paid runtime is required.