ultralytics/ultralytics v8.4.82

v8.4.82 - Fix Axelera export deleting its output when run from the model directory (#24965)

on GitHub

🌟 Summary

🔧 v8.4.82 is mainly a stability and usability release focused on fixing YOLO26 Axelera export issues, improving classification RAM caching, and tightening several training/data handling edge cases.

📊 Key Changes

• 🚀 Major fix for YOLO26 Axelera exports

  • Fixed a bug where Axelera export could accidentally delete its own output files when run from the model’s directory.
  • This was the key issue behind failed exports showing errors like “output model too small” even when compilation had actually succeeded.
  • The fix now uses a safer temporary compile folder and adds export serialization to avoid conflicts from shared process state.

• 🔄 Axelera export behavior restored for end2end=False

  • A temporary restriction added in the previous change was removed.
  • YOLO26 Axelera exports can again work with end2end=False where supported, avoiding unnecessary breakage in existing workflows such as Ultralytics Platform jobs.

• 🧠 Classification cache='ram' re-enabled with a memory-safe design

  • Classification training can now use RAM caching again without the previous memory growth problem.
  • Images are decoded once into a shared memory buffer instead of being copied separately into each worker.

• 🛑 Training now fails fast when all labels are empty

  • If a detection dataset has no usable labels at all, training now stops immediately with a clear error instead of just warning and failing later in confusing ways.

• 🖼️ Image format support cleanup

  • Added proper fallback loading for .heif images.
  • Removed the invalid .jpeg2000 extension from supported image format lists.

• 🧹 Python reliability improvements

  • Fixed several mutable default argument definitions across metrics, TAL, augmentations, YOLOE, and SAM-related code.
  • These bugs were subtle, but could cause shared state to leak across calls in long-running sessions.

• 📚 Docs and workflow polish

  • Clarified stream=True behavior in predict mode.
  • Documented MLFLOW_KEEP_RUN_ACTIVE for MLflow users.
  • Updated citations for YOLOv7, YOLOv9, YOLOv10, and YOLO12 docs.
  • Simplified Slack release/CI notifications for cleaner team alerts.

🎯 Purpose & Impact

• ✅ More reliable Axelera exports for YOLO26

  • This is the most important change in the release.
  • Users exporting YOLO26 models for Axelera hardware should see far fewer mysterious export failures, especially in automated or isolated environments like the Ultralytics Platform.

• ⚙️ Safer production workflows

  • The Axelera fix prevents exported models from being silently removed during cleanup.
  • That means more dependable deployment pipelines and less debugging time.

• 💾 Better memory efficiency for classification training

  • Re-enabling RAM caching can improve training speed while avoiding the old worker duplication problem.
  • This is especially helpful for users training classification models on larger datasets.

• 🧪 Earlier and clearer failure signals

  • Catching all-empty labels at the start helps users fix dataset problems right away instead of encountering vague downstream errors later.

• 📷 Smoother handling of real-world image files

  • Better HEIF support means fewer surprises when working with photos from modern devices.

• 🛠️ Improved long-running stability

  • The default-argument fixes reduce the chance of strange behavior caused by shared internal state, which matters most in notebooks, services, and repeated training/inference sessions.

Overall, v8.4.82 is less about new models and more about making existing YOLO26 export and training workflows more dependable 📦✨

What's Changed

• Guard YOLO26 Axelera end2end exports by @glenn-jocher in #24962
• Restore Axelera end2end=false exports by @glenn-jocher in #24963
• Fix mutable default args in metrics and tal by @raimbekovm in #24631
• Fix mutable default args in augment, YOLOE, and SAM by @raimbekovm in #24630
• Restore fail-fast when training labels are all empty by @AffanBinFaisal in #24562
• Docs: document MLFLOW_KEEP_RUN_ACTIVE env var for MLflow integration by @raimbekovm in #24602
• docs: update YOLOv7, YOLOv9, YOLOv10 and YOLO12 citations by @banu4prasad in #24501
• docs: clarify stream=True behavior by @seungcle in #24657
• Drop phantom jpeg2000 extension and fix heif loader fallback by @raimbekovm in #24594
• Refactor comments in tests/init.py for clarity by @penampungdipek-creator in #24651
• Fix classification cache='ram' leak via shared memory tensor by @raimbekovm in #24670
• Simplify Slack notification messages by @glenn-jocher in #24964
• Fix Axelera export deleting its output when run from the model directory by @glenn-jocher in #24965

New Contributors

• @seungcle made their first contribution in #24657
• @penampungdipek-creator made their first contribution in #24651

Full Changelog: v8.4.81...v8.4.82