ultralytics 8.4.64

v8.4.64 - Tracker ReID ONNX encoders autodownload (#24774)

on Python PyPI

🌟 Summary

Ultralytics v8.4.64 makes tracking easier and more reliable 🎯—especially with new auto-downloaded YOLO26 ReID ONNX encoders—while also improving QNN export, multi-GPU training stability, logging, and export robustness 🚀

📊 Key Changes

• Tracking got a major usability upgrade with auto-downloaded YOLO26 ReID ONNX encoders 🤖📦
  You can now use built-in tracking ReID models like yolo26n-reid.onnx through yolo26x-reid.onnx without manually downloading or exporting them first.

• Tracking ReID matching is more reliable when appearance features are missing 🔍
  TrackTrack now treats missing embeddings as “unknown” instead of assuming they are bad matches, so it can fall back to motion cues more intelligently.

• QNN export for Qualcomm devices was significantly improved 📱⚡
  QNN models now export as a single self-contained *_qnn.onnx file instead of a folder, with metadata embedded inside. The release also fixes architecture mapping, improves backend loading compatibility, and updates quantization behavior for better Snapdragon deployment.

• YOLO26x distributed training stability was fixed 🧠🖥️
  A DDP deadlock issue affecting some multi-GPU training runs was resolved by restoring handling for unused parameters in conditional branches.

• MLflow failures no longer crash training 📉🛡️
  If MLflow tracking setup or logging fails, training now continues instead of aborting the run.

• TensorFlow export subprocess calls are safer 🔐
  Edge TPU and TensorFlow.js export commands now avoid shell-based path handling issues, reducing problems with unusual file paths and improving security.

• Progress/logging output was cleaned up 🖥️✨
  Fixed premature 100% progress display and console log duplication issues, especially useful in platform or remote logging environments.

• Version checking is more accurate ✅
  parse_version() now consistently returns 3-part version tuples, fixing incorrect version comparisons like 6.0 vs 6.0.0.

• FP16 quantization/export reliability was improved ⚙️
  ONNX mixed-precision conversion now uses the correct input name and sample input shape, helping TensorRT/ModelOpt workflows work more consistently.

• Docs were refreshed across several areas 📘
  Updates include clearer tracking/ReID docs, QNN docs, Conda install guidance, hyperparameter tuning explanations, TrackZone behavior, custom trainer checkpoint loading, OpenVINO benchmark references, and terminal visualization guidance.

🎯 Purpose & Impact

• Easier multi-object tracking setup 🚀
  The headline change removes a common setup headache: users can now enable tracking ReID with ready-made YOLO26 ONNX encoders directly, making advanced tracking more accessible to both beginners and production teams.

• Better tracking quality in difficult scenes 🎥
  The ReID fallback improvement should reduce bad associations when objects are briefly occluded or appearance features are unavailable.

• Simpler Qualcomm deployment 📱
  QNN export is now easier to manage and deploy thanks to the single-file output format and improved compatibility across Snapdragon targets.

• More dependable training at scale 🖥️
  Teams training larger YOLO26 models on multiple GPUs should see fewer hangs and more stable runs.

• Fewer pipeline interruptions 🛠️
  MLflow and export-related fixes help ensure optional integrations do not stop core training or deployment workflows.

• Cleaner user experience ✨
  Console logging and progress bar fixes make training output easier to trust and monitor, especially in shared platforms and dashboards.

• Lower friction for installation and documentation 📚
  Updated guides make setup, tuning, export, and custom workflows easier to understand and reproduce.

In short, v8.4.64 is a tracking-focused quality-of-life release with especially strong benefits for users working with YOLO26 tracking, Qualcomm QNN deployment, and stable training/export pipelines 🚀

What's Changed

• Fix parse_version to always return a 3-tuple by @bujna94 in #24680
• Update OpenVINO benchmarks version with openvino==2026.2.0 by @lakshanthad in #24691
• Fix MLflow callback crashing training when tracking backend setup fails by @raimbekovm in #24705
• docs: clarify tuning iterations and population size by @xiazl1993 in #24706
• Use argv lists for TensorFlow export subprocess calls by @raimbekovm in #24767
• Add note on reloading models with custom trainer classes by @Harshxth in #24739
• fix(trackers): TrackTrack ReID falls back to motion for missing-feature pairs by @Laughing-q in #24768
• Fix DDP deadlock for YOLO26x: re-add find_unused_parameters by @MeVeryHandsome in #24620
• Fix premature 100% in TQDM and ANSI clear-line leak in ConsoleLogger by @glenn-jocher in #24773
• Exclude boot/firmware partitions from SystemLogger drive list by @glenn-jocher in #24764
• Fix QNN HTP arch mapping, monolithic EP loading, and A16W8 quantization by @glenn-jocher in #24775
• Specify calibration data shape for FP16 quantization by @Y-T-G in #24779
• Improve Conda quickstart guide by @raimbekovm in #24781
• Improve terminal inference results guide by @raimbekovm in #24778
• Convert QNN export to a single ONNX file by @glenn-jocher in #24780
• Refactor TrackZone guide: fix FAQ example and document zone region behavior by @raimbekovm in #24772
• Update conda CUDA install pin to pytorch-cuda=12.1 by @raimbekovm in #24782
• Tracker ReID ONNX encoders autodownload by @onuralpszr in #24774

New Contributors

• @xiazl1993 made their first contribution in #24706
• @bujna94 made their first contribution in #24680
• @MeVeryHandsome made their first contribution in #24620
• @Harshxth made their first contribution in #24739

Full Changelog: v8.4.63...v8.4.64