ultralytics 8.4.37

v8.4.37 - `ultralytics 8.4.37` NDJSON-based multidataset hyperparameter tuning (#24192)

on Python PyPI

🌟 Summary

Ultralytics v8.4.37 is a quality + workflow-focused release: the tag PR itself is a version bump, while the main substance is improved hyperparameter tuning (now NDJSON-based for multi-dataset runs), better handling of class imbalance, stronger training reliability, and clearer docs/UI guidance. 🚀

📊 Key Changes

• (Priority note) Current PR #24192 by @glenn-jocher: release tag/version update only (8.4.36 → 8.4.37) 📦

  • No direct runtime/model logic change in this PR alone.

• Major tuning upgrade (PR #24179 by @Laughing-q) 🧠

  • Hyperparameter tuning logs moved from CSV to tune_results.ndjson.
  • Better support for multi-dataset tuning with per-dataset fitness tracking.
  • Plot/output naming updated (e.g., tune_fitness.png), and MongoDB sync now aligns with local NDJSON logs.

• Class imbalance support in training (PR #23565 by @ahmet-f-gumustas) ⚖️

  • Added new cls_pw hyperparameter to weight underrepresented classes more during detection training.
  • Default is off (0.0), so existing behavior stays unchanged unless enabled.

• Training stability + robustness fixes 🛡️

  • First-epoch checkpoint is now saved even if EMA has invalid values early on (PR #24170 by @Laughing-q).
  • Fixed local zip dataset regression in HUB/Platform training (safe_download path handling) (PR #24185 by @glenn-jocher).

• Evaluation and CI reliability improvements ✅

  • compute_ap precision edge-case fix for more robust AP calculation (PR #24175 by @Laughing-q).
  • CI/Docker benchmark threshold updates to reduce false failures.

• Cleaner distributed training logs (PR #24177 by @Laughing-q) 🧹

  • Reduced duplicate model info prints in DDP/multi-process training.

• Documentation and platform UX improvements 📚

  • Correct task-specific .load() weights in segment/OBB docs.
  • OpenVINO links updated for YOLO26 optimization notebooks.
  • Platform docs updated for new Settings > API Keys flow.
  • Quickstart diagram made interactive for easier navigation.

🎯 Purpose & Impact

• For ML practitioners tuning across datasets: much better experiment tracking and analysis thanks to NDJSON + per-dataset records. 📈
• For teams with imbalanced data: cls_pw can improve rare-class learning without changing your whole pipeline. 🎯
• For production/research training: fewer broken runs and better checkpoint safety in edge cases. 🔁
• For CI and benchmarking users: fewer flaky failures from minor threshold drift. 🧪
• For new users: clearer docs and safer examples reduce silent misconfigurations and confusion. 🙌

What's Changed

• Add class weights support for handling class imbalance in training by @ahmet-f-gumustas in #23565
• Fix AP calculation precision in compute_ap by @Laughing-q in #24175
• Fix: Allow checkpoint save for first epoch even when EMA contains NaN/Inf by @Laughing-q in #24170
• Fix duplicate model info print for DDP training by @Laughing-q in #24177
• Fix benchmark verbose for Dockerfile-nvidia-arm64 by @Laughing-q in #24181
• docs: fix wrong weights in segment and obb .load() examples by @amanharshx in #24180
• Label Docs non-text code fences by @glenn-jocher in #24176
• docs: update OpenVINO notebook links to YOLO26 optimization by @easyrider11 in #24182
• Simplify engine resume tests by @fcakyon in #24183
• Fix HUB training regression for local zip datasets by @glenn-jocher in #24185
• replace journey diagram with interactive workflow graph on platform quickstart by @raimbekovm in #24186
• ultralytics 8.4.37 NDJSON-based multidataset hyperparameter tuning by @Laughing-q in #24179
• improve trainer callbacks docs with precise descriptions by @raimbekovm in #24122
• ultralytics 8.4.37 NDJSON-based multidataset hyperparameter tuning by @glenn-jocher in #24192

New Contributors

• @easyrider11 made their first contribution in #24182

Full Changelog: v8.4.36...v8.4.37