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fix(deps): update dependency ultralytics ( 8.4.62 → 8.4.67 ) - #410

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fix(deps): update dependency ultralytics ( 8.4.62 → 8.4.67 )#410
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This PR contains the following updates:

Package Type Update Change OpenSSF
ultralytics (changelog) project.dependencies patch ==8.4.62 → ==8.4.67 OpenSSF Scorecard

Release Notes

ultralytics/ultralytics (ultralytics)

v8.4.67: - Add opt-in weights_only model loading via ULTRALYTICS_SAFE_LOAD (#​24829)

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🌟 Summary

v8.4.67 is a small but meaningful release focused on safer model loading 🔒, plus an important TT100K dataset label fix 🛣️ and a batch of documentation/link cleanups 📚.

📊 Key Changes

  • 🔒 New opt-in safe model loading via ULTRALYTICS_SAFE_LOAD (@​glenn-jocher, PR #​24829)

    • Ultralytics added a new environment flag, ULTRALYTICS_SAFE_LOAD=true, that enables a safer way to load model checkpoints.
    • When enabled, checkpoint loading uses PyTorch’s newer weights_only=True behavior and only rebuilds known Ultralytics and PyTorch model classes from an automatically generated allow-list.
    • This replaces the older unused SafeUnpickler approach.
    • Important: this is off by default, so existing workflows keep working as before.

  • 🛣️ TT100K dataset class list corrected to the official 221-category set (@​glenn-jocher, PR #​24718)

    • The TT100K.yaml dataset config had duplicated and incorrect class names.
    • That caused some class IDs to silently lose labels during conversion, even though training still proceeded.
    • The release now aligns TT100K with the official upstream category list, including missing real classes.

  • 📘 Rust inference docs updated to ultralytics-inference 0.0.21 (@​onuralpszr, PR #​24825)

    • Documentation examples now point to the latest Rust inference crate version.

  • 🔗 Many documentation links were refreshed and corrected (@​glenn-jocher, PR #​24824 and #​24817)

    • Updated links across integrations, datasets, examples, and guides.
    • Includes a fix for the CoreML NMS glossary link and many canonical URL updates.

🎯 Purpose & Impact

  • 🔐 Safer checkpoint loading for security-conscious users

    • The new opt-in safe loading mode helps reduce risk when loading model files by allowing only recognized model components.
    • This is especially useful for teams working in stricter environments or aligning with newer PyTorch defaults.
    • Because it is optional, users with custom or forked architectures are not disrupted.

  • ♻️ Better future compatibility with PyTorch

    • PyTorch 2.6+ is moving toward weights_only loading by default.
    • This update helps Ultralytics stay aligned with that direction while preserving backward compatibility today.

  • 🚫 Less chance of silent dataset errors

    • The TT100K fix is a big quality improvement for anyone training on that traffic-sign dataset.
    • Previously, some categories could effectively receive no labels without an obvious crash.
    • Now, dataset conversion and training should be much more trustworthy 🎯

  • 🧰 Minimal disruption for most users

    • No major model architecture changes were introduced in this tag.
    • Standard loading behavior remains unchanged unless you explicitly enable safe loading.
    • Most other changes are documentation polish, so the release is low-risk and practical ✅

  • 📚 Clearer onboarding and fewer broken docs

    • Updated links and version references make tutorials and integration guides easier to follow, especially for newer users.

If you want, I can also provide a one-paragraph release note version or a developer-focused summary of v8.4.67.

What's Changed

Full Changelog: ultralytics/ultralytics@v8.4.66...v8.4.67

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

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🌟 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

Full Changelog: ultralytics/ultralytics@v8.4.65...v8.4.66

v8.4.65: - QNN channel-last + semantic ArgMax performance improvements (#​24790)

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🌟 Summary

v8.4.65 is mainly a mobile inference performance release 📱⚡, with the biggest upgrade improving QNN exports for Snapdragon NPUs and speeding up semantic segmentation outputs by moving costly work inside the model graph.

📊 Key Changes

  • 🚀 Major QNN export performance upgrade in PR #​24790 by @​glenn-jocher

    • QNN exports now use channel-last (NHWC) input, which better matches Qualcomm Hexagon NPU hardware and camera-native image layouts.
    • This avoids extra data reordering at inference time, reducing overhead on both the app side and the NPU side.
    • Implemented through new export wrappers like QNNModel instead of fragile post-export graph editing.

  • 🧠 Semantic segmentation exports are much faster on-device

    • For QNN and CoreML semantic models, export now embeds ArgMax / class-map generation directly in the graph.
    • Instead of returning large floating-point logits that apps must decode afterward, exported models can now return a compact per-pixel class map directly.
    • New ClassMapModel wrapper handles this behavior during export.

  • 📱 Better mobile runtime compatibility

    • Semantic predict/validation code was updated so Ultralytics can correctly handle both:
      • traditional logits outputs
      • new exported class-map outputs
    • This makes the faster export behavior work more cleanly across deployment and evaluation workflows.

  • 📉 Measured impact highlighted in docs

    • QNN semantic performance became much more stable, replacing previously erratic decoding times with far more predictable behavior.
    • CoreML semantic exports also saw notable end-to-end speedups from in-graph class-map generation.

  • 🛠️ RKNN export improvements

    • RKNN export now properly supports FP16 intermediate ONNX export.
    • Added RKNN INT8 export test coverage to improve reliability.
    • RKNN export docs/tables were updated accordingly.

  • 🐳 Docker build reliability improvements

    • Dockerfiles now use more cache-conscious install patterns and extra cleanup steps to reduce disk pressure during builds.
    • The Docker workflow was simplified by removing an extra assistant-trigger step.

  • 📚 Docs and export guidance improvements

    • Expanded and refreshed docs for CoreML and QNN, including clearer deployment guidance and performance notes.
    • Export docs now better explain YOLO26 end-to-end detection output formats.
    • Tracking docs now clarify support for OBB models as well.
    • Rust inference docs were updated to ultralytics-inference version 0.0.19.

  • ✅ CI and workflow fixes

    • Docs redeploys now trigger more reliably when documentation changes land on main.
    • CUDA training tests are now explicitly skipped on Jetson devices, making CI behavior cleaner and easier to maintain.

🎯 Purpose & Impact

  • ⚡ Faster mobile inference on Snapdragon devices
    Users deploying YOLO on Qualcomm hardware should see lower overhead and better real-world efficiency, especially in camera-based apps where image buffers are already channel-last.

  • 🧩 Less app-side postprocessing work
    By returning semantic class maps directly from exported models, apps no longer need to spend as much CPU time decoding huge segmentation outputs.

  • 📈 Better and more stable semantic segmentation performance
    This is especially important for real-time mobile experiences, where unpredictable postprocessing delays can cause lag or jitter.

  • 🔒 More robust export pipeline
    Replacing manual ONNX graph surgery with clean wrapper modules makes exports easier to maintain and less error-prone over time.

  • 📱🍎 Benefits extend beyond QNN
    CoreML semantic exports also gain from the same in-graph class-map idea, so Apple-device deployment gets a speed boost too.

  • 🛠️ Improved reliability for deployment workflows
    Better RKNN testing, cleaner Docker builds, and more dependable docs publishing all reduce friction for developers working across edge and production environments.

  • 👥 Broad user impact

    • Mobile and edge developers get the biggest benefit from this release.
    • Semantic segmentation users should see the clearest speed and usability gains.
    • General users also benefit from clearer docs, cleaner CI, and more reliable export behavior.

In short: v8.4.65 is a strong deployment-focused release 🎉, with the standout improvement being faster, more hardware-friendly QNN exports and smarter semantic segmentation outputs for mobile AI.

What's Changed

Full Changelog: ultralytics/ultralytics@v8.4.64...v8.4.65

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

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🌟 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

New Contributors

Full Changelog: ultralytics/ultralytics@v8.4.63...v8.4.64

v8.4.63: - NVIDIA TensorRT 11 support with ModelOpt FP16 and INT8 quantization (#​24735)

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🌟 Summary

🚀 Ultralytics v8.4.63 mainly adds TensorRT 11 export support with FP16 and INT8 quantization via NVIDIA ModelOpt, while also expanding built-in multi-object tracking with several new tracker options and improving reliability, performance, and docs.

📊 Key Changes

  • 🧠 Major export upgrade: TensorRT 11 support

    • The standout update in this release is PR #​24735 by @​onuralpszr.
    • Ultralytics now supports exporting models to NVIDIA TensorRT 11, which had previously broken due to API changes in TensorRT 11.
    • For FP16 and INT8 export on TensorRT 11, Ultralytics now uses NVIDIA ModelOpt instead of the older TensorRT builder flags and calibrator interface.
    • This keeps export working across:
      • TensorRT 7–10 with the legacy path
      • TensorRT 11 with the new strongly-typed ModelOpt path
    • Tested successfully for:
      • fp32
      • fp16
      • int8
      • including INT8 with dynamic shapes on TensorRT 11 ✅

  • ⚡ Better quantization workflow for modern NVIDIA deployment

    • FP16 is now applied by baking mixed precision into the ONNX graph before engine build.
    • INT8 is now applied through explicit quantization in the ONNX graph with calibration data.
    • This is especially important because TensorRT 11 removed the old methods many exporters depended on.

  • 🎯 Big tracking expansion: 4 new built-in trackers

    • PR #​24371 added four new multi-object trackers alongside BoT-SORT and ByteTrack:
      • OC-SORT
      • Deep OC-SORT
      • FastTracker
      • TrackTrack
    • These are now documented and wired into the tracking system with YAML configs like:
      • ocsort.yaml
      • deepocsort.yaml
      • fasttrack.yaml
      • tracktrack.yaml

  • 🏃 Tracking docs and selection guidance improved

    • Tracking docs were expanded to explain the strengths of each tracker and how to choose between them.
    • This makes the tracking feature much easier to use for both beginners and advanced users.

  • 📹 Video stream loading is safer

    • PR #​24749 improves cleanup when stream initialization fails.
    • If one stream fails while others already opened, Ultralytics now properly closes those partially-opened resources instead of leaking threads or capture handles.

  • ⚡ AI Gym pose workflow is faster

    • PR #​24744 reduces repeated GPU-to-CPU syncs in the workout monitoring loop by transferring keypoints to CPU in one go instead of piece by piece.
    • Same behavior, better efficiency.

  • 🧪 Validation mixed precision handling simplified

    • PR #​24736 consolidates validation autocast into one cleaner scope during training validation.
    • This helps keep mixed-precision behavior simpler and more robust.

  • 📝 Documentation improvements

    • Added new Rust inference documentation for running YOLO models through ONNX Runtime without Python.
    • Expanded tracking docs and reference pages.
    • Updated TensorRT and Jetson docs to explain TensorRT 11 behavior and DLA limitations.
    • Refreshed guides like Coral Edge TPU and semantic image search.

🎯 Purpose & Impact

  • 🚀 TensorRT 11 users can export again

    • This is the biggest user-facing change in the release.
    • If you deploy on modern NVIDIA systems using TensorRT 11, exports that were failing should now work again.
    • This is especially valuable for production inference pipelines and edge/server deployment.

  • ⚙️ Future-proofs NVIDIA deployment

    • TensorRT 11 changed how precision and quantization are handled.
    • By moving to a ModelOpt-based workflow, Ultralytics stays compatible with newer NVIDIA tooling instead of relying on removed APIs.

  • 💾 Smaller, faster engines remain accessible

    • FP16 and INT8 exports are still available even with TensorRT 11’s breaking changes.
    • That means users can continue optimizing for:
      • faster inference
      • lower memory use
      • better deployment efficiency

  • 📦 Dynamic INT8 export support is especially useful

    • Supporting INT8 with dynamic shapes on TensorRT 11 can help users deploying across varying input sizes without giving up quantization benefits.

  • 👀 Tracking becomes more flexible for real-world scenarios

    • The new trackers give users more choices depending on their needs:
      • simple baseline tracking
      • crowded-scene tracking
      • appearance-aware tracking
      • occlusion-heavy tracking
    • This can improve results in surveillance, sports, traffic, and retail use cases.

  • 🔒 More stable long-running applications

    • Stream cleanup improvements reduce the chance of lingering resources in apps that open cameras or network streams.
    • This matters most for production or multi-stream systems.

  • ⚡ Small but meaningful performance wins

    • AI Gym and validation updates help reduce overhead and improve runtime efficiency without changing how users interact with the API.

Overall, v8.4.63 is a strong deployment-focused release 📦—with the headline improvement being restored and modernized TensorRT 11 export support, plus a major boost to tracking capabilities and several reliability/performance refinements.

What's Changed

Full Changelog: ultralytics/ultralytics@v8.4.62...v8.4.63

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@jazzlyn-bot
jazzlyn-bot Bot force-pushed the renovate/ultralytics-8.x branch from fc16fef to ebcb32b Compare June 10, 2026 14:44
@jazzlyn-bot jazzlyn-bot Bot changed the title fix(deps): update dependency ultralytics ( 8.4.62 → 8.4.63 ) fix(deps): update dependency ultralytics ( 8.4.62 → 8.4.64 ) Jun 10, 2026
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jazzlyn-bot Bot force-pushed the renovate/ultralytics-8.x branch 2 times, most recently from e5bcc8d to 162cf74 Compare June 11, 2026 15:25
@jazzlyn-bot jazzlyn-bot Bot changed the title fix(deps): update dependency ultralytics ( 8.4.62 → 8.4.64 ) fix(deps): update dependency ultralytics ( 8.4.62 → 8.4.65 ) Jun 11, 2026
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jazzlyn-bot Bot force-pushed the renovate/ultralytics-8.x branch from 162cf74 to 3085e79 Compare June 12, 2026 02:06
@jazzlyn-bot jazzlyn-bot Bot changed the title fix(deps): update dependency ultralytics ( 8.4.62 → 8.4.65 ) fix(deps): update dependency ultralytics ( 8.4.62 → 8.4.66 ) Jun 12, 2026
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jazzlyn-bot Bot force-pushed the renovate/ultralytics-8.x branch 3 times, most recently from 5db7a7d to d88ff8c Compare June 13, 2026 13:16
@jazzlyn-bot
fix(deps): update dependency ultralytics ( 8.4.62 → 8.4.67 )
ef6d465 
| datasource | package     | from   | to     |
| ---------- | ----------- | ------ | ------ |
| pypi       | ultralytics | 8.4.62 | 8.4.67 |
@jazzlyn-bot
jazzlyn-bot Bot force-pushed the renovate/ultralytics-8.x branch from d88ff8c to ef6d465 Compare June 14, 2026 02:08
@jazzlyn-bot jazzlyn-bot Bot changed the title fix(deps): update dependency ultralytics ( 8.4.62 → 8.4.66 ) fix(deps): update dependency ultralytics ( 8.4.62 → 8.4.67 ) Jun 14, 2026
@jazzlyn-bot
jazzlyn-bot Bot merged commit 29b978f into main Jun 14, 2026
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jazzlyn-bot Bot deleted the renovate/ultralytics-8.x branch June 14, 2026 13:22
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