fix(deps): update dependency ultralytics ( 8.4.58 → 8.4.60 ) - #402

v8.4.60: - ultralytics 8.4.60 ONNX INT8 export (#​24666)

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🌟 Summary

Ultralytics v8.4.60 is mainly about adding ONNX INT8 export 🎉, making it easier to create smaller, faster deployment models with built-in calibration support, while also including a few helpful export, training, and documentation fixes.

📊 Key Changes

• 🚀 Major new feature: ONNX int8=True export

  • You can now export models like YOLO26 to INT8 ONNX using ONNX Runtime static quantization.
  • This uses the same familiar export flow as other INT8 formats, including data for calibration dataset selection and fraction for using only part of the dataset.
  • Exported files now clearly save as *_int8.onnx.

• 🔄 Shared INT8 calibration pipeline

  • The new ONNX INT8 export reuses the existing calibration dataloader logic instead of introducing a separate workflow.
  • Calibration reader code is now shared between ONNX and QNN export, reducing duplication and making behavior more consistent.

• 📘 Much better ONNX export documentation

  • ONNX INT8 support was added across export docs, ONNX integration docs, exporter examples, macros, reference pages, and the tutorial notebook.
  • This makes the new feature easier to discover and use correctly.

• ⚙️ RKNN export now supports the standard half argument

  • RKNN exports now officially support half=True, and this becomes the default floating-point path for supported Rockchip hardware.
  • This makes RKNN behavior more consistent with other export formats.

• 🐛 Segmentation training fix for polygons on image borders

  • A fix to segment2box ensures polygon points lying exactly on image edges are no longer dropped.
  • This helps prevent bad bounding boxes and training issues for objects that touch the image border.

• 📝 Auto-annotate docs updated

  • Documentation now correctly includes SAM 3 in the supported SAM model list.
  • The documented default output_dir for auto-annotation was corrected.

• 🧹 Docs metadata cleanup

  • Placeholder documentation metadata was replaced with meaningful defaults, improving generated docs quality.

🎯 Purpose & Impact

• 🎯 Faster and lighter ONNX deployment

  • The headline feature is ONNX INT8 export, which can help reduce model size and improve inference efficiency on supported runtimes and hardware.
  • This is especially useful for edge devices, production services, and resource-constrained deployments.

• 🛠️ Simpler export workflow

  • Users already familiar with Ultralytics INT8 export options will find ONNX INT8 works in a similar way, so there is less new syntax or tooling to learn.

• 🔒 More reliable maintenance and consistency

  • Sharing calibration logic between ONNX and QNN reduces duplicate code, which usually means fewer bugs and easier long-term support.

• 📈 Better user experience for deployment

  • Clearer docs and examples should make it easier for both new and experienced users to adopt ONNX INT8 export successfully.

• 🤖 Improved hardware export support

  • The RKNN half=True update helps Rockchip deployments behave more predictably and aligns them better with common export expectations.

• 🖼️ More accurate segmentation training

  • The border-polygon fix can improve training data handling for segmentation datasets where objects touch image edges, avoiding accidental quality loss.

Overall, v8.4.60 is a deployment-focused release 🌍, with ONNX INT8 export as the standout improvement and several supporting fixes that improve reliability, documentation, and hardware export consistency.

What's Changed

• Allow half arg for RKNN export by @​glenn-jocher in #​24660
• Fix SAM model list and default output dir in auto-annotate docs by @​raimbekovm in #​24662
• Fix segment2box dropping polygon vertices on image border by @​raimbekovm in #​24655
• ultralytics 8.4.60 ONNX INT8 export by @​glenn-jocher in #​24666

Full Changelog: ultralytics/ultralytics@v8.4.59...v8.4.60

v8.4.59: - Validate RKNN INT8 calibration dataset path (#​24659)

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🌟 Summary

Ultralytics v8.4.59 improves Rockchip RKNN export with safer and clearer INT8 quantization support, making deployment on Rockchip edge devices more reliable and easier to use 🚀

📊 Key Changes

• ✅ RKNN INT8 calibration file is now validated before export proceeds

  • The release’s main follow-up change from @​glenn-jocher checks that the internally generated calibration image list actually exists before loading the RKNN Toolkit.
  • This helps catch setup issues earlier, with clearer error messages instead of failing later in a more confusing way.

• 🧠 Simpler RKNN INT8 workflow for users

  • Users continue to provide a standard YOLO dataset YAML with data=... during export.
  • Ultralytics now handles conversion of that dataset into RKNN’s internal dataset.txt calibration file automatically.
  • dataset.txt stays an internal implementation detail, not something users are expected to manage directly.

• ⚡ New RKNN INT8 export support introduced in this release cycle

  • RKNN export now supports:
    • int8=True
    • data=...
    • fraction=...
  • This enables INT8-quantized RKNN model export, which is especially important for Rockchip devices that require INT8 models.

• 📱 Expanded support for INT8-only Rockchip chips

  • Devices such as RV1103, RV1106, RV1103B, and RV1106B are now supported through INT8 export.
  • The exporter also warns and auto-adjusts when a selected chip requires int8=True.

• 📚 Documentation and export tables updated

  • RKNN docs now clearly explain:
    • floating-point export behavior for FP16-capable targets
    • when int8=True is required
    • use of data and fraction for calibration
  • Export reference tables and tutorial content were updated to reflect the new RKNN options.

• 🛡️ Better RKNN API error handling

  • Internal RKNN API calls now check return values more carefully.
  • Failures in config, ONNX loading, build, or export are surfaced as clearer runtime errors.

🎯 Purpose & Impact

• More reliable RKNN exports 🔒

  • Validating the calibration file up front reduces frustrating export failures and makes debugging easier.

• Easier INT8 quantization for everyone 🙌

  • Users can stick to the familiar Ultralytics pattern of passing data=coco8.yaml or another dataset YAML, without learning RKNN-specific file conventions.

• Better support for edge deployment on Rockchip hardware 📦

  • INT8 export opens the door to more Rockchip NPUs, including chips that cannot use floating-point RKNN models.

• Cleaner user experience 🧹

  • The release hides low-level RKNN details from users and keeps the interface aligned with other Ultralytics export formats.

• Potential performance and compatibility gains 🚀

  • INT8 models are often smaller and better suited for constrained edge devices.
  • For supported Rockchip targets, this can improve deployability and help users target hardware that previously wasn’t practical with RKNN export.

• Lower risk of misconfiguration ⚠️

  • Clearer warnings, updated docs, and improved validation make it less likely that users will choose unsupported settings by accident.

If helpful, I can also provide a 1-paragraph release note version or a developer-focused changelog summary for v8.4.59.

What's Changed

• ultralytics 8.4.59 Rockchip RKNN INT8 export option by @​glenn-jocher in #​24656
• Validate RKNN INT8 calibration dataset path by @​glenn-jocher in #​24659

Full Changelog: ultralytics/ultralytics@v8.4.58...v8.4.59