🌟 Summary (single-line synopsis) Ultralytics v8.4.19 focuses on much more reliable Ultralytics Platform training sync (especially model tracking via model_id), plus a few quality improvements for SAM outputs, lightweight model stability, and clearer YOLO26 end-to-end docs 🚀 📊 Key Changes 🔴 Most important (current PR #23761 by @glenn-jocher): Platform training model_id fix Training callbacks now consistently pass model_id during model uploads and training events. Platform callback state was refactored into a single trainer.platform context (instead of scattered trainer fields), including: model_id last upload time cancellation status console/system loggers Upload and event flow was tightened for better session continuity, cancellation handling, and final result linking. Platform URL handling now better supports server-adjusted model slugs (for example when names auto-increment). 🧠 SAM prediction cleanup (#23751 by @Laughing-q) Added NMS in SAM3SemanticPredictor postprocessing and feature-inference paths. Reduces duplicate overlapping detections and keeps boxes/masks aligned. 🛡️ PSA attention edge-case fix (#23758 by @Y-T-G) Prevents zero attention heads in PSABlock for very small channel configs. Improves robustness for lightweight/custom model scales. 📘 Docs clarification for end-to-end models (#23720 by @raimbekovm) Clarifies that in end-to-end mode (YOLO26, YOLOv10), max_det and agnostic_nms are supported directly. Notes that setting end2end=False is mainly for enabling traditional iou-based NMS behavior. ⚙️ CI maintenance GitHub Actions artifact upload/download actions were bumped to newer major versions (#23748, #23749). African Wildlife dataset tutorial video embed updated in docs (#23757). 🎥 🎯 Purpose & Impact For Ultralytics Platform users: ✅ Training runs should now be more reliable to track end-to-end, with fewer mismatches between uploaded checkpoints and the correct model session. This is the biggest practical improvement in this release. For production and team workflows: 📡 Better callback state management means cleaner event handling, better cancellation behavior, and improved confidence that results and artifacts land in the right place. For segmentation users (SAM): 🎯 Cleaner predictions with fewer duplicate overlaps can improve downstream usability and visual quality. For custom/small models: 🧱 Fewer architecture edge-case failures when using very small widths or low channel counts. For all users reading docs: 📚 Clearer expectations around end-to-end inference/validation args in YOLO26 and YOLOv10 reduce confusion and setup mistakes. What's Changed Document max_det and agnostic_nms support in end2end mode by @raimbekovm in https://github.com/ultralytics/ultralytics/pull/23720 Bump actions/download-artifact from 7 to 8 in /.github/workflows by @dependabot[bot] in https://github.com/ultralytics/ultralytics/pull/23749 Bump actions/upload-artifact from 6 to 7 in /.github/workflows by @dependabot[bot] in https://github.com/ultralytics/ultralytics/pull/23748 Add NMS to SAM3SemanticPredictor to eliminate overlapping boxes by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/23751 Add https://youtu.be/EXYB-dbgJjY to docs by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/23757 Fix division by zero in PSABlock num_heads by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/23758 ultralytics 8.4.19 Platform training pass model ID fix by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/23761 Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.4.18...v8.4.19