ultralytics 8.4.92

v8.4.92 - Fix RKNN INT8 multi-batch export (#25094)

on Python PyPI

🌟 Summary

Ultralytics v8.4.92 improves RKNN INT8 multi-batch export reliability, strengthens inference and dataset validation, and expands YOLO26 documentation for web deployment. 🚀

📊 Key Changes

• 🔧 Fixed RKNN INT8 multi-batch export (PR #25094, @glenn-jocher)

  • RKNN calibration now uses a batch-1 ONNX graph, while the requested inference batch is passed to RKNN Toolkit through rknn_batch_size.
  • Resolves export failures when using configurations such as batch=8.
  • Adds regression coverage to verify that calibration and runtime batch handling remain correct.

• ⚡ Improved compiled model predictor reuse (PR #25092, @glenn-jocher)

  • Predictor initialization now explicitly checks whether the model is None.
  • Prevents torch.compile model wrappers from triggering unsupported truth-value checks during repeated inference.

• ✅ More accurate validation split handling (PR #25093, @glenn-jocher)

  • Dataset validation now checks the split requested by the user, such as val or test, instead of always checking val.
  • Missing or unavailable splits now produce a clear error rather than silently using another split.

• 📦 More reliable polygon-to-box conversion (PR #25086, @JESUSROYETH)

  • segment2box() now preserves the visible portion of polygons that cross image boundaries.
  • Improves bounding boxes after perspective and other augmentations, reducing shrunken or collapsed boxes.

• 🌐 Expanded LiteRT web deployment documentation (PR #25087, @onuralpszr)

  • Documents browser and Node.js inference with LiteRT.js and the official @ultralytics/yolo NPM package.
  • Includes WebGPU acceleration with CPU/WASM fallback and support for all six YOLO26 tasks.
  • Installation is documented with npm i @ultralytics/yolo @litertjs/core.

• 📚 Refreshed pose dataset documentation

  • Corrects dataset sizes, splits, keypoint details, and download requirements for COCO-Pose, COCO8-Pose, Dog-Pose, Hand Keypoints, and Tiger-Pose.
  • Adds clearer guidance for using Ultralytics Platform to annotate and train custom pose datasets.
  • Clarifies that COCO-Pose requires approximately 27 GB for its complete first download.

• 🔐 Simplified contributor license workflow

  • Replaces the third-party CLA action with the shared ultralytics/actions workflow.
  • Reduces duplicated configuration and limits workflow permissions.

🎯 Purpose & Impact

• Rockchip users can export INT8 models with larger inference batches more reliably, especially for edge deployments using RKNN hardware. 🧩
• Compiled models are safer to reuse across multiple predictions, improving stability for optimized inference workflows.
• Dataset validation errors are clearer and more trustworthy, helping users catch incorrect or missing test or val data earlier.
• Augmented segmentation data retains correct object extents, which can improve training labels and downstream detection quality near image borders.
• YOLO26 deployment is easier in browsers, enabling serverless webcam, image, and real-time inference through WebGPU or CPU/WASM fallback. 🌍
• Pose estimation documentation is more accurate and practical, making dataset selection, download planning, and training setup easier for both new and experienced users.

What's Changed

• Replace third-party CLA action by @glenn-jocher in #25084
• docs: 📝 Update LiteRT documentation to include Web deployment details and npm package installation by @onuralpszr in #25087
• Fix compiled model predictor reuse by @glenn-jocher in #25092
• Validate requested detection dataset split by @glenn-jocher in #25093
• Fix segment2box shrinking boxes when augmented polygons cross image bounds by @JESUSROYETH in #25086
• Refactor pose dataset docs by @raimbekovm in #25062
• Fix RKNN INT8 multi-batch export by @glenn-jocher in #25094

Full Changelog: v8.4.91...v8.4.92