Ultralytics YOLO

🌟 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 https://github.com/ultralytics/ultralytics/pull/24660
• Fix SAM model list and default output dir in auto-annotate docs by @raimbekovm in https://github.com/ultralytics/ultralytics/pull/24662
• Fix segment2box dropping polygon vertices on image border by @raimbekovm in https://github.com/ultralytics/ultralytics/pull/24655
• ultralytics 8.4.60 ONNX INT8 export by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/24666

Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.4.59...v8.4.60