ultralytics 8.4.114

v8.4.114 - Surface Platform error messages and stop the console-output retry loop (#25581)

on Python PyPI

🌟 Summary

v8.4.114 improves reliability across Platform workflows, exported models, validation, edge inference, and advanced vision tasks—while delivering clearer errors and faster, more robust execution. 🚀

📊 Key Changes

• Clearer Ultralytics Platform errors and quieter retries — PR #25581 by @glenn-jocher:

  • Platform URI resolution now uses GET instead of HEAD, preserving the detailed error messages returned by the Platform.
  • API errors such as invalid credentials, inaccessible datasets, and malformed pose labels now include actionable details.
  • Console-output upload failures no longer create a feedback loop where logged retry warnings trigger additional failed uploads.
  • Platform requests now stop early when no API key is available, and invalid credentials disable further attempts.

• Improved exported-model validation:

  • Static ONNX, TensorRT, OpenVINO, and similar models now automatically reuse the image size stored in export metadata.
  • Users no longer need to manually provide the exact export imgsz during validation. ✅

• More reliable model export and deployment:

  • Fixed GPU device mismatches during TorchScript inference by ensuring generated anchors follow the runtime device.
  • Prompt-free YOLOE exports now work correctly with NCNN and Paddle formats.
  • Loading a TorchScript archive as if it were a PyTorch checkpoint now produces a clearer error message.
  • Paddle export compatibility was improved for newer Python and x2paddle environments.

• Faster LiteRT CPU inference:

  • LiteRT now uses the configured number of CPU threads, enabling multi-core inference.
  • Raspberry Pi 5 LiteRT benchmarks were corrected for YOLO26n and YOLO26s, showing substantially lower latency than previously reported. ⚡

• Fixes for SAM3, visualization, and pose rendering:

  • SAM3 semantic prediction now defines the required mask threshold and avoids an AttributeError.
  • Class activation maps safely handle class IDs outside a model’s output range.
  • Pose keypoints and limbs located exactly on image borders are now rendered correctly instead of being silently dropped.

• Improved training and data pipelines:

  • BGR augmentation now applies correctly to semantic segmentation and depth training.
  • Distributed validation no longer crashes when the total batch size exceeds the number of validation images.
  • Dataset YAML validation handles empty names fields more safely.
  • Analytics line charts now accumulate counts across the configured update window instead of resetting every frame.

• Documentation and maintenance updates:

  • Added missing validation documentation for channels_last.
  • Documented class remapping and depth-loss training parameters.
  • Updated augmentation support tables for semantic and depth tasks.
  • Fixed Intel DL Streamer installation links and similarity-search examples.
  • Removed several redundant regression tests to reduce test-suite maintenance overhead. 📚

🎯 Purpose & Impact

• 🛠️ Faster troubleshooting: Platform failures now explain what went wrong, helping users fix dataset, authorization, and configuration issues without repeated trial and error.
• 🔁 More stable automation: Console logging will no longer feed failed messages back into Platform upload retries, reducing noisy logs and unnecessary network traffic.
• ✅ Simpler validation: Exported models can generally be validated without manually matching their original image size and batch settings.
• 🚀 Better edge performance: Multi-core LiteRT support can significantly improve CPU inference speed on devices such as Raspberry Pi 5.
• 📦 Broader deployment compatibility: TorchScript, NCNN, Paddle, and other export paths are more dependable across devices and Python environments.
• 🎯 More robust vision workflows: SAM3, pose visualization, CAM generation, depth, semantic segmentation, and distributed validation now handle common edge cases more gracefully.
• 📖 Clearer documentation: Users can more easily discover supported arguments and follow current Intel and similarity-search setup instructions.

What's Changed

• Enable multi-core Google LiteRT CPU inference by @lakshanthad in #25562
• Update Raspberry Pi 5 benchmarks with Google LiteRT using multi-cores by @lakshanthad in #25564
• Fix AttributeError on mask_threshold in SAM3 semantic prediction by @JESUSROYETH in #25563
• Fix installation guide links in Intel DL Streamer documentation by @onuralpszr in #25574
• Remove tests added alongside small bug-fix PRs by @Laughing-q in #25578
• Reuse imgsz from export metadata when validating static exported models by @synml in #25559
• Document channels_last in the validation args table by @synml in #25561
• Apply the bgr augmentation to semantic and depth training by @raimbekovm in #25580
• Fix IndexError in class_activation_map with out-of-range classes by @leoventuroso in #25582
• Fix Annotator.kpts() dropping keypoints and limbs on image borders by @JESUSROYETH in #25565
• Fix: Analytics line chart total_counts resets before update_graph consumes it by @Zenka737 in #25567
• Fix NCNN and Paddle export of prompt-free YOLOE models by @Y-T-G in #25568
• Fix DDP error when total batch size is higher than validation images by @Y-T-G in #25576
• Document depth loss gains and attribute semantic and depth in the argument tables by @raimbekovm in #25577
• fix Python syntax error in similarity-search solution by @RizwanMunawar in #25579
• Fix TorchScript GPU inference device mismatch by @Y-T-G in #25569
• Surface Platform error messages and stop the console-output retry loop by @glenn-jocher in #25581

New Contributors

• @leoventuroso made their first contribution in #25582

Full Changelog: v8.4.113...v8.4.114