ultralytics 8.4.66

v8.4.66 - Add `nvidia-ml-py` to pyproject.toml (#23922)

on Python PyPI

🌟 Summary

Ultralytics v8.4.66 improves built-in NVIDIA GPU monitoring reliability 🔋, adds better semantic segmentation class filtering and metrics 🧠, and enhances CoreML semantic exports on iPhone with full-resolution outputs 📱.

📊 Key Changes

• Main update: NVIDIA GPU monitoring is now more reliable 🚀

  • nvidia-ml-py was added as a standard dependency by @glenn-jocher.
  • GPU stats collection now uses NVIDIA’s NVML library directly instead of trying to install support at runtime.
  • This affects built-in GPU reporting in utilities like auto device selection and system logging.

• Semantic segmentation got a meaningful feature upgrade 🎯

  • The classes filter now works properly for semantic segmentation during:
    • dataset loading
    • prediction
    • validation
    • metric reporting
  • Non-selected classes are cleanly ignored instead of being mixed into results.
  • Added safeguards for unsupported cases like single_cls=True in semantic segmentation.

• Semantic segmentation metrics are more accurate 📈

  • mIoU now ignores classes that are not actually present in the ground truth.
  • Per-class reporting was improved so summaries better reflect what was really evaluated.

• CoreML semantic exports now keep full-resolution maps 📱✨

  • CoreML exports for semantic segmentation now output full-resolution semantic maps instead of lower-resolution stride-8 maps.
  • This means much sharper segmentation overlays on Apple devices like iPhones.

• Updated iPhone CoreML benchmark docs 📊

  • Performance numbers were refreshed using optimized profile-mode measurements.
  • Documentation now better reflects real YOLO26 CoreML behavior, especially for semantic models.

• TensorRT 11 export flow was simplified ⚙️

  • The separate half argument was removed from ONNX precision conversion.
  • TensorRT 11 export now has a cleaner path for FP16 and INT8 generation.
  • INT8 calibration preprocessing was streamlined for cleaner and more maintainable export behavior.

• Documentation improvements across YOLO26 guides 📝

  • The Model Testing guide was rewritten for clarity and accuracy.
  • The YOLO26 Training Recipe guide was improved to better explain how official checkpoints were trained.
  • Small fixes were also made to JupyterLab and SAM 3 docs.

• CI and packaging cleanup 🛠️

  • CI dependency installs now retry automatically, reducing flaky failures.
  • Since nvidia-ml-py is now a standard dependency, extra manual installs were removed from CI and Docker setup.

🎯 Purpose & Impact

• More dependable GPU stats for users with NVIDIA hardware ✅
  Users should see fewer issues with built-in GPU monitoring, logging, and hardware-aware utilities because the required NVIDIA package is now installed up front instead of handled on the fly.

• Better usability for semantic segmentation workflows 🎨
  If you work with semantic segmentation and only care about certain classes, filtering now behaves much more intuitively during prediction and evaluation.

• More trustworthy segmentation metrics 📏
  Reported semantic segmentation scores are now less misleading, especially on datasets where some classes are absent or filtered out.

• Sharper CoreML semantic results on iPhone and Apple devices 🍎
  Semantic masks should look much cleaner and less blocky, improving visual quality for mobile deployment.

• Cleaner export experience for TensorRT 11 ⚡
  Developers exporting models for NVIDIA deployment get a simpler and more maintainable precision conversion workflow.

• Clearer docs for a broader audience 📚
  New and experienced users alike should find YOLO26 testing, training, and deployment guidance easier to follow.

Overall, v8.4.66 is a practical quality-focused release: it strengthens GPU monitoring, improves semantic segmentation behavior, and brings better mobile export quality without changing the core user workflow.

What's Changed

• Remove unused half argument and optimize INT8 calibration in TensorRT 11 export by @Laughing-q in #24795
• Improve model testing guide structure and fix validation metric claims by @raimbekovm in #24800
• Export full-resolution semantic maps to CoreML by @glenn-jocher in #24799
• Refresh iPhone CoreML benchmarks with profile-mode measurements by @glenn-jocher in #24804
• Fix evaluate link target in JupyterLab integration docs by @raimbekovm in #24808
• Clarify multiple bounding boxes behavior in SAM3 semantic predictor docs by @raimbekovm in #24807
• Add retry wrapper to flaky CI installs by @glenn-jocher in #24809
• Adapt classes filter to semantic segmentation task by @lmycross in #24806
• Improve YOLO26 training recipe guide structure and accuracy by @raimbekovm in #24805
• Add nvidia-ml-py to pyproject.toml by @glenn-jocher in #23922

Full Changelog: v8.4.65...v8.4.66