0.3.0

What's changed

⚠️ Breaking changes

• StateDetector(backend, roi, states) is now StateDetector(roi, states, *, backend=None). Without backend=, the detector uses SqueezeNet on ncnn, loaded once per process and shared.
• Creating the first detector without backend= loads the model. It blocks on file I/O and can raise BackendError, so create detectors in an executor, not in an event loop.
• ncnn is a regular dependency (ncnn>=1.0.20261020) and import tarsius always imports it. The ncnn extra is gone, and tarsius[ncnn] only gives a pip warning.
• NcnnBackend.id is now squeezenet11/2 because fp16 is off. Stored originals are re-extracted on load, so nothing needs to be taught again.

✨ New features

• #8 feat!: ncnn as a regular dependency, StateDetector picks and shares its backend @RaHehl

🧰 Maintenance

• #5 chore: drop CodSpeed, strict mypy, manual publish run @RaHehl

📚 Documentation

• #7 docs: evaluate history with StateDetector, report unknown and timing @RaHehl

Full Changelog: 0.2.0...0.3.0

0.2.0

What's changed

⚠️ Breaking changes

• Reference is removed, with Features.reference(), Reference.to_bytes()/from_bytes() and FeaturesError. Classifier.add() takes Features only. References are re-extracted from stored frames instead.
• load_image(draft_side=) is now load_image(max_side=), and the output is at most max_side for every format. Features extracted after load_image() change for the same backend id.
• majority() returns a Result instead of str | None.
• Blank references and invalid regions raise BlankRegionError and RoiError, both ValueError subclasses. A frame that shows nothing raises BlankFrameError, a BlankRegionError subclass.

✨ New features

• #2 feat: add StateDetector, shared frame normalization and region outline @RaHehl

🚀 Enhancements

• #4 ci: test on Python 3.15 and list it as supported @RaHehl

📚 Documentation

• #1 docs: add tarsier logo @RaHehl
• #3 docs: explain the name @RaHehl

Full Changelog: 0.1.0...0.2.0

0.1.0

First release.

• Classifier with k-nearest confirmed references per state and explicit unsure and not_ready results
• NcnnBackend: SqueezeNet 1.1 features on ncnn (extra tarsius[ncnn]), model converted from the original release in CI
• EdgeBackend: numpy-only baseline
• Regions as fractions or grid cells, reduced JPEG decoding, reference serialization

ncnn stays an optional extra until an ncnn release no longer requires opencv-python, see the README.