fix(deps): update dependency ultralytics ( 8.4.70 → 8.4.72 ) - #422

v8.4.72: - Fix TensorRT INT8 export crash on RTX cards exposing the NvTensorRTRTX EP (#​24876)

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🌟 Summary

v8.4.72 is a small but important stability release that mainly fixes a TensorRT INT8 export crash on some RTX GPUs 🚀, while also improving export environment reliability and cleaning up docs/CI.

📊 Key Changes

• Fixed TensorRT INT8 export crashes on certain RTX cards 🔧

  • The most important change in this release is from PR #​24876 by @​glenn-jocher.
  • Exporting models with format="engine", int8=True could fail on some RTX GPUs, including cases where ONNX Runtime exposed both TensorRT execution providers.
  • Ultralytics changed the INT8 calibration step to use CUDA with CPU fallback instead of triggering conflicting TensorRT provider combinations.

• Improved TensorRT/ONNX export image compatibility for CUDA 12 🐳

  • PR #​24877 pins onnxruntime-gpu to below 1.27.0 in the export Docker image.
  • This avoids breakage caused by newer ONNX Runtime GPU builds that now expect CUDA 13, while the export image still uses CUDA 12.8.

• GitHub Actions checkout updated from v6 to v7 ⚙️

  • PR #​24873 refreshes CI workflows to a newer actions/checkout version.
  • This is a maintenance update for the project’s automation pipeline.

• Documentation updates and cleanup 📚

  • PR #​24863 refreshes the Queue Management guide with a newer YOLO26 video and updated wording.
  • PR #​24875 simplifies the Triton C++ example README by removing extra contributor footer content.
  • PR #​24851 adds clearer page titles for account settings and YOLO configuration docs, helping site clarity and SEO.

🎯 Purpose & Impact

• More reliable INT8 TensorRT exports on RTX hardware 💡

  • Users exporting optimized TensorRT engines should see fewer failures on affected NVIDIA RTX systems.
  • This is especially helpful for developers deploying compact, fast INT8 inference pipelines.

• Better support for modern RTX environments 🖥️

  • The fix avoids a low-level ONNX Runtime provider conflict that was causing exports to crash before the model was even fully built.
  • In simple terms: INT8 export now works more reliably on hardware that previously broke unexpectedly.

• Safer Docker-based export workflows 🛡️

  • Pinning onnxruntime-gpu prevents version mismatches that could break ONNX inference and export tests inside CUDA 12 environments.
  • This should make CI and container-based deployment setups more predictable.

• No major model architecture changes 📌

  • This release does not introduce a new model or training feature.
  • The focus is on export stability, environment compatibility, and documentation polish.

• Practical benefit for users ✅

  • If you use Ultralytics for training and then export to TensorRT INT8 for production, this release is worth adopting.
  • If you mainly use standard training or FP16 export, the impact is smaller but still positive thanks to general reliability improvements.

What's Changed

• Add https://youtu.be/TEVPiGCxB0o to docs by @​RizwanMunawar in #​24863
• docs: small cleanup triton contributions by @​onuralpszr in #​24875
• Pin onnxruntime-gpu<1.27.0 for CUDA 12 export image by @​glenn-jocher in #​24877
• Bump actions/checkout from v6 to v7 in /.github/workflows by @​UltralyticsAssistant in #​24873
• Fix duplicate title tags on Spanish account settings and YOLO config pages by @​miles-deans-ultralytics in #​24851
• Fix TensorRT INT8 export crash on RTX cards exposing the NvTensorRTRTX EP by @​glenn-jocher in #​24876

Full Changelog: ultralytics/ultralytics@v8.4.71...v8.4.72

v8.4.71: - Official C++ Examples refactor 🐪 (#​24869)

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🌟 Summary

Ultralytics v8.4.71 is mainly a release-tag update 📦, but the most important underlying change is a major refactor of the official C++ examples that makes YOLO deployment in C++ much more unified, easier to use, and more capable across backends 🚀

📊 Key Changes

• Release bump to 8.4.71 🔖

  • The current PR by @​glenn-jocher primarily updates the package version from 8.4.70 to 8.4.71.
  • No direct Python API, model architecture, or runtime behavior changes are introduced in this version-bump PR itself.

• Big C++ examples refactor landed in this release 🛠️

  • C++ inference examples were reorganized into a single examples/cpp/ structure instead of being scattered across many separate folders.
  • Multiple backends are now aligned under one shared design:
    • OpenCV-DNN
    • ONNX Runtime
    • LibTorch
    • MNN
    • OpenVINO
    • Triton
  • Shared header-only utilities were added for:
    • task detection
    • post-processing
    • rendering/annotation
    • CLI handling
    • COCO fallback names

• Much broader C++ task support 🎯

  • The refactored examples now support all major YOLO tasks across C++:
    • detection
    • segmentation
    • pose
    • OBB
    • classification
    • YOLO26 semantic segmentation
  • They also support multiple model generations, including YOLOv8, YOLO11, and YOLO26.

• Automatic task and model behavior detection 🤖

  • Many C++ examples can now automatically determine the task from model metadata or output shapes.
  • This reduces manual setup and makes examples easier to reuse with different exported models.

• More consistent C++ developer experience 🧰

  • Unified build targets like yolo_<backend>
  • Standardized CLI arguments such as --model and --source
  • Cleaner READMEs and better docs across examples
  • Default examples now commonly point to lightweight YOLO26 models

• Ultralytics Platform GPU docs expanded ☁️

  • Documentation now includes two new Blackwell GPU options on the Ultralytics Platform:
    • RTX PRO 4000 Blackwell with 24 GB VRAM
    • RTX PRO 5000 Blackwell with 48 GB VRAM
  • Platform cloud GPU counts were updated accordingly across docs.

🎯 Purpose & Impact

• For most users: this release is mostly about packaging and delivery** ✅

  • v8.4.71 helps tools and environments track the newest Ultralytics build correctly.
  • It improves release consistency without forcing disruptive API changes.

• For C++ developers: this is the real headline** 💡

  • The refactor makes C++ deployment much easier to understand and maintain.
  • Instead of learning different example layouts for each backend, users get a more unified workflow.
  • This should lower friction for deploying YOLO models in production C++ systems.

• For teams using multiple model types: better flexibility** 🔄

  • Supporting more tasks and generations in a common C++ structure means fewer custom modifications are needed when switching models.
  • That can save engineering time and reduce mistakes.

• For YOLO26 adoption: stronger deployment support** 🚀

  • Adding clearer C++ coverage for YOLO26, including semantic segmentation, helps bring the latest recommended Ultralytics model into more real-world applications.

• For Ultralytics Platform users: more cloud hardware choices** ⚡

  • The added Blackwell GPU options expand available training hardware in the docs and platform guidance.
  • This can help users choose a better balance of performance, VRAM, and cost.

In short: the tag PR itself is just a version bump, but v8.4.71 packages a meaningful C++ usability upgrade and refreshed platform GPU documentation 🎉

What's Changed

• Add RTX PRO 5000 and RTX PRO 4000 Blackwell to Platform GPU docs by @​glenn-jocher in #​24868
• Refactor and unify C++ inference examples 🐪 by @​onuralpszr in #​24867
• Official C++ Examples refactor 🐪 by @​glenn-jocher in #​24869

Full Changelog: ultralytics/ultralytics@v8.4.70...v8.4.71