ultralytics 8.4.53

v8.4.53 - `ultralytics 8.4.53` Retry CUDA backend memory errors during training (#24569)

on Python PyPI

🌟 Summary

Ultralytics v8.4.53 mainly improves training reliability on NVIDIA GPUs by automatically recovering from more CUDA memory-related failures, while also polishing semantic segmentation stability, documentation clarity, and CI robustness 🚀

📊 Key Changes

• Smarter GPU memory recovery during training 🧠⚡
  The biggest change in this release, from PR #24569 by @glenn-jocher, expands the existing auto-retry logic for large-batch GPU training.

  • Previously, Ultralytics could retry after a standard CUDA out-of-memory error.
  • Now it also retries for related backend memory failures such as:
    • CUDNN_STATUS_INTERNAL_ERROR
    • “unable to find an engine to execute this computation”
  • When this happens during the first epoch on a single GPU, training can automatically reduce batch size and continue instead of crashing.

• Semantic segmentation validation is more reliable 🖼️✅
  PR #24552 fixes issues in semantic validation so metrics are preserved correctly after evaluation.

  • Prevents stats from being cleared too early
  • Makes mIoU handling more robust when some classes are missing
  • Avoids NaN-style metric problems in sparse-class cases

• Segmentation loss is numerically more stable 🛠️📈
  PR #24554 improves Dice loss stability by forcing key intermediate calculations to use float32.

  • Helps avoid precision-related instability
  • Especially useful in mixed-precision or lower-precision training setups

• Much better docs for prediction outputs, especially semantic segmentation 📚
  PR #24558 is a major documentation improvement.

  • Clearly explains the Results object for each task
  • Documents the new/important semantic_mask output in detail
  • Adds comparisons between instance segmentation and semantic segmentation
  • Clarifies output shapes, dtypes, masks, polygons, boxes, and per-task behavior

• Broader YOLO26 and semantic task visibility across docs and metadata 🌍
  Several updates improve how Ultralytics presents current capabilities:

  • Package metadata now reflects YOLO26, Platform, and oriented object detection
  • Project classifier changed to Production/Stable
  • Many docs now consistently mention semantic segmentation as a first-class supported task

• CI and test workflow became more stable 🧪
  Multiple changes reduce avoidable test failures:

  • IMX export test is skipped in broader CI where it was causing instability
  • Training-related tests are skipped on Raspberry Pi, where they are not practical

• Small but useful documentation and packaging fixes ✨

  • README task banner now renders correctly on PyPI
  • OBB validation docs now note the true default confidence used to reduce memory usage
  • Minor doc fixes for tracking links, benchmark hardware, glossary links, and formatting

🎯 Purpose & Impact

• Fewer frustrating training crashes on GPUs 💪
  If you train with aggressive batch sizes, this release can help Ultralytics recover automatically from more real-world CUDA memory failures instead of stopping unexpectedly.

• Better experience for users pushing hardware limits 🚀
  Large-batch training on single GPUs should now be more forgiving, especially when failures come from CUDA backend libraries rather than a plain out-of-memory exception.

• More dependable semantic segmentation workflows 🧠
  Users working with semantic segmentation should see:

  • more stable validation metrics
  • fewer confusing missing/invalid values
  • improved training stability from safer Dice loss calculations

• Easier to understand model outputs 🔍
  The new docs make it much clearer what prediction results contain for each task, which is especially helpful for:

  • beginners trying to use Results
  • developers building pipelines around semantic outputs
  • teams comparing instance vs semantic segmentation behavior

• Better confidence in production use ✅
  The “Production/Stable” metadata and improved CI signal a continued push toward reliability for real deployments.

• Low risk release with practical quality-of-life improvements 🙂
  This is not a major architecture or model release, but it delivers meaningful gains in stability, clarity, and usability, especially for training and semantic segmentation users.

What's Changed

• Clean up semantic CI follow-up by @glenn-jocher in #24544
• Fix tasks banner not rendering on PyPI by @raimbekovm in #24545
• Update pyproject metadata for YOLO26 and Platform by @glenn-jocher in #24547
• Fix Cityscapes glossary link by @glenn-jocher in #24550
• Fix semantic docs typo by @Laughing-q in #24551
• Fix semantic nt_per_image printing issue by @Laughing-q in #24552
• Fix NaN Dice loss during validation by @Laughing-q in #24554
• Skip training on Raspberry Pi Test CIs by @lakshanthad in #24556
• Add segmentation results output details to task docs by @glenn-jocher in #24558
• Note OBB validation conf default of 0.01 by @raimbekovm in #24561
• Fix stale line anchor for track_high_thresh in track docs by @raimbekovm in #24559
• Docs: align markdown table columns by @Laughing-q in #24566
• Update CI tests by @Laughing-q in #24567
• ultralytics 8.4.53 Retry CUDA backend memory errors during training by @glenn-jocher in #24569

Full Changelog: v8.4.52...v8.4.53