ultralytics 8.4.48

v8.4.48 - `ultralytics 8.4.48` Fix Platform training edge cases (#24431)

on Python PyPI

🌟 Summary

Ultralytics v8.4.48 is a stability-focused release that mainly improves training reliability on Ultralytics Platform and makes failure cases much clearer, with supporting fixes for benchmarking and reporting. 🛠️✅

📊 Key Changes

• (Top priority) Platform training edge-case fixes from PR #24431 by @glenn-jocher:

  • Added guards for empty semantic-mask batches to prevent crashes in segmentation workflows. 🎭
  • Added safe handling for empty RLE keypoint masks in loss computation to avoid invalid operations. 🤸
  • Training now fails with a clear error if no checkpoint (best/last) is saved, instead of failing later with confusing behavior. 💾
  • Model loading now throws a clearer error if a checkpoint depends on a missing ultralytics.* module, with guidance to retrain/use current official models. 📦

• Benchmark/export robustness fix from PR #24418 by @lakshanthad:

  • data is now only passed to export formats that actually support it, reducing export benchmark failures across formats. 📊

• Platform metrics correctness fix from PR #24425 by @mykolaxboiko:

  • training_complete.bestEpoch now reports the real best epoch (from early stopping), not just the final epoch trained. 🎯

• Docs improvement for multi-GPU training from PR #24422 by @artest08:

  • Added a custom trainer guide section showing how to use SyncBatchNorm safely in multi-GPU setups (especially helpful for small per-GPU batch sizes). 📚🧠

• Maintenance/docs housekeeping

  • Dependabot pip updates switched from daily to monthly (PR #24411 by @glenn-jocher). 📅
  • CI docs updated with corrected iOS App Store link and table cleanup (PR #24430 by @glenn-jocher). 📱

🎯 Purpose & Impact

• More reliable training pipelines on edge cases (empty masks, empty keypoint visibility), especially important for automated or mixed datasets. 🚀
• Faster debugging and clearer failures when training output/checkpoint loading goes wrong. This saves developer and MLOps time. ⏱️
• More trustworthy Platform reporting with accurate bestEpoch, improving experiment tracking and decision-making. 📈
• Better cross-format benchmarking stability, so export testing is less brittle. 🔄
• Overall, this release is less about new model features and more about making existing YOLO workflows safer, clearer, and production-friendlier. ✅

What's Changed

• Run Python dependency updates monthly by @glenn-jocher in #24411
• Fix benchmarks passing unsupported data arg to export formats by @lakshanthad in #24418
• Fix Platform bestEpoch reporting wrong value at training_complete by @mykolaxboiko in #24425
• docs: Add SyncBatchNorm example to custom trainer guide by @artest08 in #24422
• Add iOS App Store link to CI docs by @glenn-jocher in #24430
• ultralytics 8.4.48 Fix Platform training edge cases by @glenn-jocher in #24431

Full Changelog: v8.4.47...v8.4.48