🌟 Summary (single-line synopsis)

Ultralytics v8.4.8 makes end-to-end (NMS-free) YOLO26/YOLOv10 inference actually honor max_det and agnostic_nms, giving you predictable control over how many detections you get and how classes are handled ✅🚀

📊 Key Changes

• End2end now supports max_det + agnostic_nms (PR #23396 by @Y-T-G) 🎛️
  • Adds a safe set_head_attr(**kwargs) helper to set head/last-layer attributes like end2end, max_det, agnostic_nms 🧩
  • When end2end=True, predict and val now push max_det + agnostic_nms into the model head, so these flags actually take effect ⚙️
  • Adds agnostic_nms support inside the Detect head (affects top-k selection logic used in end-to-end mode) 🎯
  • Export pipeline also carries agnostic_nms into the exported head for better parity between Python and exported models 📦
• Clearer end2end controls across CLI/docs 📚
  • Adds end2end to config/defaults and docs for predict/val/export args 🧾
  • Expands tests to run export/predict/val with end2end=True/False ✅🧪
• Notable additional fixes included in this tag (smaller but impactful) 🛠️
  • Export reliability: exporter auto-disables end2end for formats that don’t support required ops (RKNN/NCNN/ExecuTorch/Paddle/IMX) and stores end2end in metadata 🧰
  • YOLOE/YOLO-World training usability: allows passing a YAML file (or Path) for multi-source data configs 🗂️
  • Docs improvements: new Compare section + cleaner navigation; sitemap auto-fills missing pages 🧭
  • Stability fixes: rotated-OBB small-box robustness, DDP unwrap fixes for pose validation, PyTorch 1.9 compatibility adjustment, OpenVINO/ONNX example bugfixes 🔧

🎯 Purpose & Impact

• More predictable end-to-end inference results 🧠
  • max_det now reliably limits outputs in end2end mode (no more “why didn’t it cap?” confusion).
• Better class-agnostic behavior when you need it 🧹
  • agnostic_nms becomes meaningful in end2end workflows, helpful when classes overlap heavily or you want “best boxes regardless of class”.
• Closer parity between Python and exported deployments 🚚
  • Exported models are less likely to behave differently from local inference due to missing head settings.
• Fewer surprises across formats & environments ✅
  • Safer exporting to constrained runtimes, more robust training/validation in edge cases, and improved docs discoverability for new users.

If you’re using YOLO26 (the recommended model family) in end-to-end mode, this release is a practical quality-of-life upgrade. 🚀🙂

What's Changed

• Update optimizer selection strategy by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/23350
• Fix broken links in NVIDIA Jetson documentation by @lakshanthad in https://github.com/ultralytics/ultralytics/pull/23362
• Add ability to override end2end mode by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/23333
• Update small obb candidates selection by @lmycross in https://github.com/ultralytics/ultralytics/pull/23378
• Update coco12-formats predict tests by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/23374
• fix openvino sample for coordinates by @jules-ai in https://github.com/ultralytics/ultralytics/pull/23366
• Fix index issue of strides in select_candidates_in_gts by @PT0X0E in https://github.com/ultralytics/ultralytics/pull/23369
• Add note about SAM3 tokenizer error due to incorrect clip package by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/23368
• Fix stride_val type issue in STAL by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/23390
• Export non-e2e version by default for rknn/executorch/paddle/imx formats by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/23312
• fix: add unwrap_model to fix DDP AttributeError by @lmycross in https://github.com/ultralytics/ultralytics/pull/23392
• Fix: use torch.no_grad for torch==1.9.0 to resolve training issue by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/23395
• Simplify Docs nav by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23398
• Fix swapped input_height and input_width in ONNX example - Fixes #23126 by @ahmet-f-gumustas in https://github.com/ultralytics/ultralytics/pull/23402
• Add models compare pages to Docs by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23406
• Fix visual prompt training for YOLOE26 by @ShuaiLYU in https://github.com/ultralytics/ultralytics/pull/23401
• Add https://youtu.be/7lZa3Yi2kbo to docs by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/23388
• Classify train fixes by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23420
• Fix/yoloe text model attribute in yoloe trainer from scratch by @ShuaiLYU in https://github.com/ultralytics/ultralytics/pull/23428
• Fix names generation in SAM3VideoSemanticPredictor by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/23434
• Update NVIDIA Jetson Thor benchmarks with YOLO26 by @lakshanthad in https://github.com/ultralytics/ultralytics/pull/23443
• Save close_mosaic as integer in by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/23435

New Contributors

• @PT0X0E made their first contribution in https://github.com/ultralytics/ultralytics/pull/23369

Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.4.7...v8.4.8