Ultralytics YOLO

🌟 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