ultralytics 8.4.34

v8.4.34 - `ultralytics 8.4.34` Multi-dataset support for hyperparameter tuning (#24067)

on Python PyPI

🌟 Summary

Ultralytics v8.4.34 is a tuning and stability-focused release 🚀, led by a major new feature: multi-dataset hyperparameter tuning in one run, plus several important reliability fixes and broad YOLO26 documentation updates.

📊 Key Changes

• 🧠 Major feature (PR #24067 by @Laughing-q): Multi-dataset hyperparameter tuning

  • model.tune() now accepts data as either a single dataset or a list.
  • During each tuning iteration, training runs across each dataset, then combines results.
  • Fitness is averaged across datasets, so tuning decisions reflect overall performance, not just one dataset.
  • Tests were updated to validate this workflow (coco8.yaml + coco8-grayscale.yaml).
  • Version bumped to 8.4.34.

• 🛡️ Training resume stability fix (PR #24085 by @Y-T-G)

  • Prevents loss spikes after resume on small datasets by keeping AdamW’s exp_avg_sq state in FP32.
  • Reduces risk of unstable training when loading checkpoints.

• 🔒 Thread-safe ONNX export (PR #24092 by @glenn-jocher)

  • Added export locking so concurrent threads cannot collide in PyTorch’s global ONNX exporter state.
  • Includes a regression test for parallel export safety.

• ⚙️ Robustness fixes in core runtime

  • DDP cleanup now safely handles command-generation failures (PR #24056 by @nameearly).
  • AAttn fixed for non-divisible dim/num_heads cases to avoid shape/group crashes (PR #24114 by @ZoomZoneZero).
  • crop_mask() now clamps negative coordinates before cropping for safer segmentation postprocessing (PR #24115 by @Y-T-G).
  • draw_specific_kpts() now respects user-provided keypoint index order and handles missing confidence values safely (PR #24099 by @onuralpszr).

• 📚 Documentation and ecosystem refresh (many PRs)

  • Large migration to YOLO26 references and fresh benchmarks across Jetson + SAM docs.
  • Jetson setup improved with missing cuDSS dependency instructions for Torch 2.10.0 (PR #24081 by @lakshanthad).
  • DeepStream version mapping/docs links corrected and expanded for newer JetPack versions (PRs #24141, #24142).
  • Ultralytics Platform docs improved (Smart Annotation with SAM + YOLO, split redistribution UX, account settings clarity, banner/link updates).

🎯 Purpose & Impact

• Better real-world tuning quality 🎯
  Multi-dataset tuning helps teams optimize one model for mixed or varied data domains (for example, color + grayscale, or multiple data sources), improving generalization and reducing overfitting to a single dataset.

• More reliable training and export workflows ✅
  Resume training is more stable, distributed cleanup is safer, and ONNX export is more dependable in threaded environments—especially useful in production pipelines.

• Improved deployment and edge guidance 📱
  Updated YOLO26 Jetson benchmarks and setup docs make edge deployment decisions more current and practical.

• Cleaner user experience in docs and platform onboarding ✨
  Better Smart Annotation and dataset split guidance can reduce setup friction and speed up annotation/training workflows on the Ultralytics Platform.

What's Changed

• Update NVIDIA Jetson AGX Orin benchmarks with YOLO26 by @lakshanthad in #24068
• update SAM docs with YOLO26 references and fresh benchmarks by @raimbekovm in #24071
• update SAM-2 docs with YOLO26 references and fresh benchmarks by @raimbekovm in #24072
• update SAM-3 docs with YOLO26 references and correct model sizes by @raimbekovm in #24045
• Add missing cuDSS package to JetPack 6 installation guide by @lakshanthad in #24081
• Bump onnx from 1.20.0 to 1.21.0rc1 in /examples/RTDETR-ONNXRuntime-Python in the pip group across 1 directory by @dependabot[bot] in #24082
• Remove redundant ray tune test by @Laughing-q in #24088
• Docs: Redistribute Splits by @sergiuwaxmann in #24087
• Document YOLO models in platform smart annotation by @laodouya in #24037
• Docs: Update Banner by @sergiuwaxmann in #24090
• Docs: Fix Banner Link by @sergiuwaxmann in #24091
• Improve Platform Settings page tab docs and add screenshots by @yogendrasinghx in #24060
• Fix loss explosion on resume when training on small dataset by @Y-T-G in #24085
• Bump onnx from 1.21.0rc1 to 1.21.0 in /examples/RTDETR-ONNXRuntime-Python in the pip group across 1 directory by @dependabot[bot] in #24094
• Fix Sentry bugs 2026-04-01 by @glenn-jocher in #24092
• Prevent DDP resource cleanup issues when generate_ddp_command(self) r… by @nameearly in #24056
• Add https://youtu.be/Y7cfNkqSdMg to docs by @RizwanMunawar in #24098
• fix: 🐞 draw_specific_kpts to respect user-specified indices order by @onuralpszr in #24099
• docs: update synk badge with working one by @onuralpszr in #24101
• Update NVIDIA Jetson Orin NX 16GB benchmarks with YOLO26 by @lakshanthad in #24118
• Clamp coordinates in crop_mask before cropping by @Y-T-G in #24115
• Fix AAttn: resolve shape mismatches and crashes when dim is not divisible by num_heads by @ZoomZoneZero in #24114
• Update NVIDIA Jetson Orin Nano Super benchmarks with YOLO26 by @lakshanthad in #24097
• docs: update DeepStream 7.1 documentation link for JetPack 6.1 by @onuralpszr in #24141
• docs: update DeepStream installation instructions for JetPack 7.1 to include DeepStream 9.0 by @onuralpszr in #24142
• ultralytics 8.4.34 Multi-dataset support for hyperparameter tuning by @Laughing-q in #24067

New Contributors

• @yogendrasinghx made their first contribution in #24060
• @ZoomZoneZero made their first contribution in #24114
• @nameearly made their first contribution in #24056
• @laodouya made their first contribution in #24037

Full Changelog: v8.4.33...v8.4.34