🌟 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