🌟 Summary (single-line synopsis) Ultralytics v8.4.13 makes training more resilient by automatically recovering from CUDA out-of-memory (OOM) errors during the first epoch by retrying with a smaller batch size 🔁🧠🔥 📊 Key Changes Auto-retry on CUDA OOM during training (major change) 🔥🛠️ If a CUDA OOM happens in the first epoch on single-GPU, Ultralytics will retry up to 3 times, halving the batch size each time (down to 1). Training pipeline is rebuilt after batch reduction (dataloaders + optimizer + scheduler) to continue cleanly. New internal training helper 🧩 Adds a _build_train_pipeline() method to rebuild loaders/optimizer/scheduler when batch size changes (used by the new OOM recovery flow). More reliable ONNX export for OBB + NMS 📦✅ When exporting OBB (oriented bounding boxes) to ONNX with NMS enabled, simplify=True is now forced to avoid a known runtime issue (TopK-related error in some ONNX Runtime versions). DGX system detection + TensorRT handling 🖥️⚙️ Adds is_dgx() detection and uses it (along with Jetson JetPack 7) to trigger a TensorRT version check/reinstall path for better export reliability on those systems. Packaging stability fix: pin setuptools 🧰🔒 Pins build requirements to setuptools<=81.0.0 to avoid breakages introduced by newer setuptools versions (notably affecting tensorflow.js export tooling). Docs & examples refresh (YOLO26 messaging + tracking content) 📚🎥 Tracking docs now embed a newer multi-object tracking video featuring YOLO26 + BoT-SORT/ByteTrack. Exporter docs/examples updated to show YOLO26 (yolo26n.pt) and mention ExecuTorch/Axelera export options (documentation signposting). Example dependency update 🔄 Updates protobuf in the RT-DETR ONNX Runtime Python example. 🎯 Purpose & Impact Fewer training crashes for everyday users 🙌🔥 If you start training with a batch size that's slightly too large for your GPU, Ultralytics can now self-correct and continue instead of failing immediately—especially helpful for beginners and for "first-epoch spikes" in memory use. Less manual trial-and-error 🎯 Reduces the common loop of "OOM → lower batch → restart training," saving time and frustration. More dependable deployment exports 🚀 ONNX exports for OBB models with embedded NMS should work more reliably out of the box, with fewer runtime surprises. More predictable builds/CI 🧱 Pinning setuptools helps prevent sudden packaging/tooling failures across environments. Clearer guidance aligned with YOLO26 🧭 Docs and examples increasingly steer users toward YOLO26 as the recommended model for training, tracking, and export workflows. What's Changed feat: 🚀 NVIDIA DGX device variants check by @onuralpszr in https://github.com/ultralytics/ultralytics/pull/23573 Add https://youtu.be/qQkzKISt5GE to docs by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/23582 Bump protobuf from 6.31.1 to 6.33.5 in /examples/RTDETR-ONNXRuntime-Python in the pip group across 1 directory by @dependabot[bot] in https://github.com/ultralytics/ultralytics/pull/23572 docs: 📝 exporter documentation for new model formats and examples updated by @onuralpszr in https://github.com/ultralytics/ultralytics/pull/23585 Force simplify=True for OBB export with NMS by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/23580 Pin setuptools version by @Burhan-Q in https://github.com/ultralytics/ultralytics/pull/23589 ultralytics 8.4.13 Retry smaller batch on training CUDA OOM by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23590 Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.4.12...v8.4.13

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