ultralytics 8.4.150

v8.4.150 - Speed up restricted loading and restore fused checkpoints (#26155)

on Python PyPI

🌟 Summary

v8.4.150 improves secure checkpoint loading, restores compatibility with fused YOLOE models, and adds performance and documentation updates across Ultralytics. 🚀

📊 Key Changes

• ⚡ Faster restricted checkpoint loading — PR #26155 by @glenn-jocher

  • Resolves only the model classes actually referenced by a checkpoint instead of importing and scanning every supported model module.
  • Preserves existing trusted namespaces, aliases, registration locks, and allow-list behavior.
  • Restores the safe forward and forward_fuse bindings required by fused YOLOE checkpoints.
  • Improves error reporting by showing the rejected object type without calling repr() on partially reconstructed modules.
  • Adds support for legacy NumPy metadata and embedded Ultralytics CLIP objects during restricted loading.
  • Extends tests to cover legacy checkpoints, fused models, NumPy fitness values, and inference-output consistency.

• 🛡️ Safer diagnostics and Ray Tune compatibility — PR #26151

  • Prevents Sentry from capturing command-line arguments and local variables that may contain credentials.
  • Enforces Ray version 2.41.0 or newer and removes obsolete compatibility code for older Ray releases.

• 📈 More efficient RT-DETR training on dense datasets — PR #26150

  • Avoids calculating matching costs between predictions and targets from different images.
  • Reduces unnecessary cost storage and padding overhead, with the greatest benefits expected on datasets containing many objects per image, such as SKU-110K.
  • Keeps the assignment solver, losses, precision, and training configuration unchanged.

• 📚 YOLO27 preview documentation

  • Publishes a clearly labeled YOLO27 Coming Soon model page with planned architecture details, supported tasks, preliminary benchmarks, and future usage examples.
  • Adds discovery links throughout the README and documentation.
  • Clarifies that YOLO27 weights, package support, and a launch date are not currently available; YOLO26 remains the recommended released model.

• ☁️ Expanded Ultralytics Platform deployment documentation

  • Documents configurable CPU and memory resources, uptime billing, scale-to-zero behavior, and lightweight monitoring for eligible paid endpoints.
  • Explains that prediction examples and statistics are temporary and may be lost after stopping, restarting, redeploying, resizing, or replacing an endpoint.
  • Describes saving useful prediction examples to datasets for persistent storage.

• 🏆 Clearer model benchmark tables

  • Standardizes bold highlighting for best accuracy, speed, latency, size, parameter count, and computational-cost values across model documentation.
  • Updates benchmark tables for YOLO26, YOLO11, YOLOv8, YOLOv9, YOLO12, YOLOE, SAM, YOLO-NAS, and other supported families without changing the underlying results.

🎯 Purpose & Impact

• More reliable production loading: Fused YOLOE checkpoints and older Platform checkpoints can load and warm up successfully again. ✅
• Faster cold starts: Restricted loading performs less unnecessary module discovery, which should reduce startup overhead, especially in deployment environments.
• Maintained security: Loading remains restricted to trusted, explicitly permitted classes and bindings; the changes do not broadly enable arbitrary checkpoint contents.
• Better dense-object training performance: RT-DETR users working with crowded scenes may see faster training and lower memory usage, while ordinary datasets should see little or no change.
• Improved privacy: Sentry error reports are less likely to contain credentials supplied through yolo login or other local variables.
• Clearer upgrade guidance: Users can explore planned YOLO27 capabilities while understanding that it is not yet available. For current projects, use YOLO26 or deploy through the Ultralytics Platform.

What's Changed

• Docs: Deploy configuration and lightweight monitoring by @sergiuwaxmann in #26149
• Prevent Sentry credential capture and remove obsolete Ray fallback by @glenn-jocher in #26151
• Speed up RT-DETR matching on dense datasets by @fcakyon in #26150
• Support legacy NumPy metadata and CLIP in restricted loading by @glenn-jocher in #26153
• Publish YOLO27 coming-soon documentation and discovery links by @glenn-jocher in #26154
• Highlight best benchmark values across model documentation by @glenn-jocher in #26156
• Speed up restricted loading and restore fused checkpoints by @glenn-jocher in #26155

Full Changelog: v8.4.149...v8.4.150