Auspex Cursus

End-to-end polyline detection: for every detected instance the network outputs an ordered, open sequence of vertices — a path (a pipe, a drip line, a road centre line, a horizon), not a box and not a closed polygon. Each class declares its own vertex count (n_points: 2 for straight things, 8–12 for curves), so a 2-point "horizon" and a 12-point "drip line" live in the same model.

  • CursusDETR: a DETR-style network with hierarchical instance×point queries, deformable attention (pure PyTorch, no CUDA extensions), iterative refinement, denoising training and direction-invariant Hungarian matching.
  • Any annotation source: CVAT XML, LabelMe JSON, GeoJSON/Shapefile + GeoTIFF with a full georeferencing pipeline — all converted to one on-disk format (CursusJSON).
  • Two training modes behind one config switch: whole-image, or tiled with sliding-window inference and cross-tile stitching for large rasters.
  • Chamfer-mAP evaluation, ONNX export, CVAT/LabelMe/GeoJSON prediction writers (predictions can be uploaded back to CVAT as pre-labels).
  • Single & multi-GPU training (DDP), Windows / Linux, CPU-capable inference.

Install

pip install auspex-cursus

Install a CUDA build of PyTorch first (from pytorch.org) if you want GPU training.

Quickstart

auspex-cursus synth --out datasets/synth --labelspace labelspace.yaml --n-images 64
auspex-cursus convert --format cvat --src annotations.xml --images ./images \
                      --labelspace labelspace.yaml --out data.cursus.json
auspex-cursus train --cfg my_run.yaml
auspex-cursus predict --weights runs/my_run/best.pt --source ./photos --out preds \
                      --formats cursus,cvat,overlay

Every subcommand has --help.

Licensing

Prediction, validation, conversion and export are free to use. Training runs on a 30-day evaluation trial, after which it needs a licence credential — either a CURSUS- licence key or a Helion account token covering auspex-cursus:

export AUSPEX_CURSUS_LICENCE=<key>       # or a path to a file holding it

Licensing: https://heliontechltd.com/license

Distributed as compiled binaries; verification is fully offline — training machines never need network access.

Project links

Key dates

PyPI data
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  • Released:
Newer release available (0.8.2)

1 maintainer

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Avatar for sohanurislamshuvo from gravatar.com sohanurislamshuvo

Credits

Author: Md. Sohanur Islam Shuvo

License

Other/Proprietary License (Proprietary)

Requires

Python <3.14, >=3.10

Provides Extra

geo export viz dev

Classifiers

Report project as malware

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