fix(deps): update dependency ultralytics ( 8.4.13 → 8.4.14 ) - #283

v8.4.14: - ultralytics 8.4.14 Platform training cancel feature (#​23614)

Compare Source

🌟 Summary (single-line synopsis)

Ultralytics 8.4.14 adds Ultralytics Platform “Cancel training” support so you can stop runs quickly and still keep/upload partial results ⛔️📤

📊 Key Changes

• Platform-driven training cancellation (priority change) 🛑
  • Detects cancellation even before training starts (during session registration) and prevents wasted startup time.
  • Checks for cancellation at epoch end via a send-and-check response flow, then sets trainer.stop=True when cancelled=true.
  • Tracks cancellation state with trainer._platform_cancelled and logs clear messages.
  • Continues to upload partial artifacts/metadata when a run is cancelled (so you don’t lose everything).
• Faster responsiveness to stop requests between batches ⚡
  • Training loop now breaks if self.stop is set, allowing external stops (like Platform cancellation) to take effect sooner.
• YOLO26 Pose training stability fix 🧍‍♂️📈
  • Clamps negative rle_loss to zero to prevent loss from going negative and destabilizing training (helps avoid mAP dropping across epochs).
• Segmentation → bounding box conversion edge-case fix 🖼️📦
  • segment2box() now excludes points exactly on the image border after clipping, avoiding boxes incorrectly snapping to edges (better box regression + mask quality).
• Hyperparameter tuning crash fix 🧪🔧
  • Prevents TypeError when fitness is present but None during tuning (safer/cleaner tuning runs).
• Docs clarity: Params/FLOPs reporting after model.fuse() 📚
  • Adds notes explaining why Params/FLOPs in docs may differ from what you see locally (fused inference vs full training architecture).

🎯 Purpose & Impact

• Better control on Ultralytics Platform 🎛️
  • If you hit Cancel in the Platform, training should stop reliably and promptly—without waiting for a long delay.
• Lower wasted compute + cost 💸
  • Cancelled jobs exit earlier and more predictably, especially helpful for long training runs.
• Keeps useful outputs even when cancelling 📦
  • Partial uploads mean you can still inspect progress, logs, and intermediate artifacts after stopping.
• More stable YOLO26 Pose training ✅
  • Reduces risk of training “going backwards” due to negative loss behavior; improves consistency of pose metrics over epochs.
• Higher-quality results for segmentation-derived boxes 🎯
  • Fewer edge-snapped boxes after augmentations can improve both training quality and final predictions.
• Fewer interruptions in automated workflows 🤖
  • Tuning and reporting are more robust, reducing flaky failures in pipelines.

If you train via the Ultralytics Platform, this release is especially impactful due to the new cancellation behavior 🛑.

What's Changed

• Fix TypeError when fitness is None during hyperparameter tuning by @​Mr-Neutr0n in #​23603
• Clamp negative rle_loss to zero in Pose26 training by @​Mr-Neutr0n in #​23604
• Fix segment2box boundary condition for edge-snapped segment points by @​Mr-Neutr0n in #​23602
• Add footnote clarifying param/FLOP counts after model.fuse() by @​raimbekovm in #​23601
• ultralytics 8.4.14 Platform training cancel feature by @​glenn-jocher in #​23614

Full Changelog: ultralytics/ultralytics@v8.4.13...v8.4.14