ultralytics 8.4.139

v8.4.139 - Reduce validation dataloader memory with prefetch 2 per worker (#26057)

on Python PyPI

🌟 Summary

v8.4.139 improves training efficiency and reliability—especially by reducing validation memory usage—while refreshing model statistics, dataset handling, optimizer performance, and documentation. 🚀

📊 Key Changes

• Lower validation dataloader memory usage by @glenn-jocher:

  • Validation workers now prefetch 2 batches instead of 4.
  • Training loaders continue using a prefetch factor of 4.
  • This reduces queued validation data and shared-memory usage without slowing validation startup. 💾

• Faster Muon/MuSGD optimizer updates:

  • Updates are now grouped by column count and flattened according to their memory layout.
  • This avoids unnecessary tensor copies for channels-last convolution weights and preserves fused parameter updates.
  • The standalone Muon optimizer and related dead code were removed; MuSGD remains available. ⚡

• More reliable EMA checkpoints:

  • EMA attributes such as class names, class counts, strides, and class weights are now copied from the unwrapped model.
  • This fixes missing or stale metadata when using distributed training or torch.compile.
  • EMA is initialized after class weights are calculated, improving checkpoint consistency. ✅

• Correct semantic segmentation dataset detection:

  • A default masks/ directory is now recognized as PNG-mask semantic segmentation when no masks_dir entry is specified.
  • Prevents incorrect class counts and phantom background classes in affected datasets. 🎯

• Improved FP16 behavior documentation:

  • Documentation now clearly warns that quantize=16 can cast a retained PyTorch model in place.
  • A later FP32 call on the same model object may therefore use FP16-rounded weights.
  • This clarification applies to prediction and validation, including shared model objects. ⚠️

• Refreshed FLOPs and parameter reporting:

  • Published model statistics were synchronized with the current profiler.
  • Updated values cover YOLO26, YOLO11, YOLO12, YOLOv8, YOLOv5u, and other documented models.
  • Accuracy and speed benchmark values were not changed. 📊

• More robust and maintainable internals:

  • Batch-normalization fusion now uses PyTorch’s official fusion utilities.
  • SAM positional embeddings avoid nondeterministic cumulative-sum operations.
  • Temporary module aliases are now thread-safe and restore modified attributes correctly.
  • NumPy-to-tensor conversion can share CPU memory and perform a single device transfer.
  • ONNX export opset selection was simplified and made more compatible with current runtimes.

• Expanded task visibility and documentation:

  • Depth estimation is now listed consistently across the README, package description, docstrings, tutorials, and task detection messages.
  • Documentation validation now requires Zensical 0.0.58 or newer.
  • Several model benchmark tables and page frontmatter entries were corrected. 📚

🎯 Purpose & Impact

• Users training with limited system or shared memory should see fewer validation-memory spikes, particularly with many dataloader workers or large images. 🧠
• Training performance may improve for the default CUDA channels-last path because Muon updates avoid repeated copies and retain fused optimizer operations.
• Distributed and compiled training runs should produce more complete and dependable checkpoints through improved EMA metadata handling.
• Semantic segmentation workflows using a conventional root-level masks/ folder now behave as documented, avoiding incorrect output channels and class definitions.
• FP16 users should be aware that prediction or validation may modify a PyTorch model object in place; keeping separate model instances is recommended when switching between FP16 and FP32.
• Benchmark comparisons are more trustworthy because FLOPs and parameter counts now reflect current profiler behavior, although these corrected statistics may differ from earlier documentation.
• Depth estimation is easier to discover for new users, while existing model APIs and task support remain compatible. 🌊

What's Changed

• Document that a predict-time FP16 request casts the caller's PyTorch model in place by @raimbekovm in #26037
• Republish FLOPs measured with the current profiler by @raimbekovm in #26036
• Bucket Muon matrices by column count and flatten updates in memory order by @Y-T-G in #26035
• Fix semantic nc for the default masks/ folder by @laodouya in #26034
• Fix Zensical 0.0.58 docs validation by @glenn-jocher in #26041
• Replace hand-rolled torch helpers with their torch owners and drop dead code by @glenn-jocher in #26043
• Refresh EMA attributes from the unwrapped model by @glenn-jocher in #26047
• Add depth estimation to README intro paragraph by @raimbekovm in #26055
• Add depth estimation to task lists across README, docstrings and package description by @raimbekovm in #26056
• Reduce validation dataloader memory with prefetch 2 per worker by @glenn-jocher in #26057

Full Changelog: v8.4.138...v8.4.139