napari: a multi-dimensional image viewer for Python
Bibliographic record
Abstract
napari 0.6.6 ⚠️ Note: these release notes are still in draft while 0.6.6 is in release candidate testing. ⚠️ Fri, Oct 10, 2025 We're happy to announce the release of napari 0.6.6! napari is a fast, interactive, multi-dimensional image viewer for Python. It's designed for browsing, annotating, and analyzing large multi-dimensional images. It's built on top of Qt (for the GUI), vispy (for performant GPU-based rendering), and the scientific Python stack (numpy, scipy). For more information, examples, and documentation, please visit our website, https://napari.org. napari follows EffVer (Intended Effort Versioning); this is a Meso release containing awesome new features, but some effort may be needed when updating previous projects to use this version. Highlights This a small bugfix release, following up the changes in 0.6.5. Bug Fixes Fix invisible zoom box (#8344) Documentation Add release notes for v0.6.6 (docs#866) Other Pull Requests Add a new attr_to_settr utility function and simplify layer control widgets layer to widget setup (#8274) ci(dependabot): bump the actions group with 9 updates (#8324) Delay settings import to avoid circular import (#8327) Add info about conda forge manual trigger to release checklist (#8328) 3 authors added to this release (alphabetical) (+) denotes first-time contributors 🥳 Daniel Althviz Moré - @dalthviz Grzegorz Bokota - @Czaki Lorenzo Gaifas - @brisvag 6 reviewers added to this release (alphabetical) (+) denotes first-time contributors 🥳 Carol Willing - @willingc Daniel Althviz Moré - @dalthviz Grzegorz Bokota - @Czaki Lorenzo Gaifas - @brisvag Melissa Weber Mendonça - @melissawm Tim Monko - @TimMonko
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.300 | 0.276 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".