🌟 Summary ExecuTorch export lands! You can now export YOLO11/YOLO26 models to .pte with XNNPACK acceleration for fast, native on-device inference on mobile and edge devices. Plus, a stability fix for CLIP embedding extraction in VisualAI. 📱⚡🧠 📊 Key Changes ExecuTorch export (primary update) New export target: format=executorch → outputs a .pte model inside a _executorch_model/ directory Integrated into export_formats() and the exporter flow New export_executorch() method using torch.export, XnnpackPartitioner, and to_edge_transform_and_lower Saves model metadata alongside the .pte file for easy integration Backend/runtime support added in AutoBackend for .pte loading and inference dispatch Docs: New ExecuTorch integration guide and export tables updated Tests: Added CLI and matrix export tests (skipped on Windows) VisualAI stability improvement Safely detaches CLIP embeddings before converting to NumPy to avoid autograd errors and reduce memory overhead Minimal usage: CLI: yolo export model=yolo11n.pt format=executorch Python: from ultralytics import YOLO YOLO("yolo11n.pt").export(format="executorch") Helpful links: Learn more in the ExecuTorch integration docs ExecuTorch overview and usage: Deploy YOLO11 with ExecuTorch ExecuTorch project: Meta's ExecuTorch 🎯 Purpose & Impact Mobile and edge deployment made easy 🚀 One-line export to .pte for running models on iOS, Android, and embedded Linux devices via ExecuTorch Uses XNNPACK by default for optimized CPU performance on mobile Early benchmarks on devices like Raspberry Pi indicate notable speedups (around 2x in example tests), with accuracy parity Streamlined developer workflow ✅ Feels like any other Ultralytics export target; consistent UX and metadata packaging Clear docs and examples for integrating on mobile and embedded platforms Compatibility notes 🧩 Requires Python 3.10+, PyTorch ≥ 2.9, executorch and flatbuffers installed Export writes both .pte and metadata.yaml; inference via ExecuTorch runtime (not standard Python YOLO() loading) Improved stability for VisualAI 🔒 Detaching CLIP embeddings prevents gradient-related errors and reduces memory use during similarity search Overall, this release expands deployment options for YOLO11/YOLO26—especially for mobile and edge users—while improving robustness in VisualAI workflows. 🎉 What's Changed Fix VisualAI clip feature extraction numpy() grad error by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/22463 ultralytics 8.3.220 Add ExecuTorch export (.pte) with XNNPACK by @ambitious-octopus in https://github.com/ultralytics/ultralytics/pull/22244 Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.219...v8.3.220

If you use this software, please cite it using the metadata from this file.