ultralytics 8.4.68

v8.4.68 - Semantic segmentation class weights (#24843)

on Python PyPI

🌟 Summary

Ultralytics v8.4.68 is mainly a training-quality and reliability release, led by a major improvement for semantic segmentation: you can now use class weighting to better handle imbalanced datasets 🎯🧠

📊 Key Changes

• Semantic segmentation now supports class weights via cls_pw 🖼️⚖️

  • Added in PR #24843 by @Y-T-G.
  • This is the headline change in the release.
  • Previously, semantic segmentation did not support user-configurable class weighting.
  • Now, training can emphasize underrepresented classes, which is especially useful when some classes appear far less often than others.

• Default semantic training behavior stays the same 🔒

  • If cls_pw is not set, or is 0, Ultralytics keeps the previous behavior.
  • Existing Cityscapes-specific weighting is still used automatically when appropriate.
  • This means current training pipelines should continue working without surprises.

• Better weighting logic for segmentation 🧮➡️🧠

  • Semantic segmentation now uses a weighting approach based on class pixel frequency that is more suitable for dense pixel-level tasks than the method used in detection.
  • This helps avoid overly extreme weighting while still giving rare classes more importance.

• More robust handling of missing or unreadable labels/masks 🛠️

  • The new class-counting logic safely handles cases where labels or masks are missing or unreadable.
  • Instead of breaking training, it can fall back more gracefully.

• Detection classification loss can now apply class weights too 📦

  • The release also refactors class counting and weighting into reusable trainer helpers.
  • Detection training now applies computed class weights directly in the classification loss.

• Several production stability fixes 🚑

  • PR #24840 by @glenn-jocher fixes four real-world issues:
    • crash from empty prediction batches
    • corrupt cached AMP asset loading
    • truncated downloads
    • invalid non-mapping YAML files
  • These changes improve day-to-day reliability for training and inference.

• Safer model loading on older PyTorch versions 🔐

  • PR #24830 by @glenn-jocher makes restricted loading fall back cleanly on torch<2.5 instead of erroring out.
  • Helpful for older toolchains and export workflows.

• Security fix for similarity search UI 🛡️

  • PR #24828 escapes user query input properly to prevent a high-severity browser injection issue.

• Docs and platform updates 📚

  • Added Windows on Snapdragon QNN benchmarks for YOLO26n across all six tasks, showing strong NPU speedups 🚀
  • Updated Ultralytics Platform GPU pricing docs 💳
  • Large cleanup of broken links, page titles, URLs, and navigation for smoother docs browsing.

🎯 Purpose & Impact

• Better semantic segmentation on imbalanced datasets 🌍

  • If your dataset has rare classes like small road signs, defects, or medical regions, the model can now pay more attention to them during training.
  • This may improve results where minority classes were previously overlooked.

• Safer upgrade path ✅

  • Because default behavior is preserved unless you use cls_pw, most users can upgrade without changing existing results.

• More control for advanced users 🎛️

  • Researchers and practitioners training custom semantic datasets now get a useful tuning option that was missing before.

• Improved robustness in production 🏭

  • Fewer crashes from bad files, corrupt caches, malformed YAML, or odd input batches means smoother automation and more reliable pipelines.

• Broader hardware confidence 💻

  • The new Windows on Snapdragon benchmark documentation helps users evaluate YOLO26 deployment on modern ARM laptops and NPU hardware.

• Better security and compatibility 🔒

  • Safer web templates and graceful fallback behavior on older PyTorch versions reduce friction for both developers and deployers.

Overall, v8.4.68 is a practical quality release with its biggest win being more capable and customizable semantic segmentation training, plus several important fixes that make Ultralytics more stable and dependable in real-world use 🚀

What's Changed

• Skip restricted model loading on torch<2.5 by @glenn-jocher in #24830
• Fix docs broken link checks by @glenn-jocher in #24836
• docs: add Windows on Snapdragon benchmarks by @ShuaiLYU in #24834
• Update Platform GPU pricing by @glenn-jocher in #24838
• Fix ClearML project paths by @MUKILAN0608 in #24827
• Escape similarity search query by @orbisai0security in #24828
• Add https://youtu.be/zF2T17ppKIE to docs by @RizwanMunawar in #24837
• Fix four production Sentry issues: predict batch, corrupt AMP cache, truncated downloads, non-mapping YAML by @glenn-jocher in #24840
• Fix docs metadata and URL issues by @glenn-jocher in #24839
• Semantic segmentation class weights by @Y-T-G in #24843

New Contributors

• @orbisai0security made their first contribution in #24828
• @MUKILAN0608 made their first contribution in #24827

Full Changelog: v8.4.67...v8.4.68