ultralytics 8.4.76

v8.4.76 - Add multi-dataset fine-tuning via MultiTrainer (#24914)

on Python PyPI

🌟 Summary

Ultralytics v8.4.76 is headlined by a powerful new multi-dataset fine-tuning workflow 🚀, making it much easier to train one YOLO model across several datasets in one run, while also improving tracking defaults, segmentation/tracking stability, and docs clarity.

📊 Key Changes

• 🆕 Multi-dataset fine-tuning via MultiTrainer

  • You can now pass a list or tuple of datasets to model.train().
  • Ultralytics will fine-tune the same base model separately on each dataset in sequence.
  • It returns a per-dataset metrics dictionary and saves a cross-dataset results chart.
  • This expands the old RF100-specific workflow into a more general tool that works for RF100, custom dataset collections, and broader benchmarking.

• 📈 Better multi-dataset benchmarking outputs

  • Training runs are grouped into a shared multitrain directory.
  • Results now include:
    • per-dataset metrics
    • a summary JSON file
    • a bar-chart image comparing results across datasets

• 🔗 Easier Ultralytics Platform dataset/model loading

  • Platform web URLs can now be pasted directly and are automatically converted to ul:// format behind the scenes.
  • This helps streamline workflows using the Ultralytics Platform 🌐

• 🎯 Default tracker changed to tracktrack.yaml

  • TrackTrack is now the default multi-object tracker instead of BoT-SORT.
  • Users who rely on default settings may notice different tracking behavior or ID consistency.

• 🛠️ Segmentation + ReID compatibility fix

  • A fix prevents valid segmentation detections from being incorrectly filtered out when native ReID is enabled.
  • This improves reliability for advanced tracking workflows.

• 📦 DDP training metrics recovery improved

  • Training now better returns metrics in distributed training setups, rather than losing them in some multi-GPU cases.

• 🔄 Revert of recent predict() path-list loading change

  • A previous change meant to reduce memory issues for large file-path lists was reverted because it introduced breaking behavior for exported models with static batch sizes.
  • This restores previous compatibility, especially for ONNX, TensorRT, MNN, and TorchScript export use cases.

• 🧩 Solutions improvements

  • Added show_boxes to instance segmentation solutions so users can turn bounding boxes on or off.
  • Fixed RegionCounter so counts no longer remain stale on empty frames.
  • Clarified region argument docs to better match actual behavior.

• 📚 Documentation updates

  • Added ADE20K semantic segmentation benchmarks for YOLO26 models.
  • Added lightweight COCO16 / COCO32 / COCO64 test subsets to docs.
  • Tracking docs now highlight newer trackers like OC-SORT, Deep OC-SORT, FastTracker, and TrackTrack.
  • Large docs refresh for SEO, consistency, titles, and clearer guides.

🎯 Purpose & Impact

• For researchers and advanced users 🧪

  • The new multi-dataset training feature is the biggest change in this release.
  • It makes it much easier to compare how a single YOLO base model performs across many datasets without building custom scripts.
  • This is especially useful for benchmarking, transfer learning, dataset sweeps, and robustness testing.

• For teams managing many datasets 📂

  • You can now automate repeated fine-tuning runs in a cleaner, more reproducible way.
  • The saved summary plots and JSON outputs make results easier to review, share, and integrate into reporting pipelines.

• For tracking users 🚗

  • The switch to TrackTrack as the default tracker may improve out-of-the-box tracking quality, but it could also slightly change results in existing workflows that depended on default behavior.
  • If you want the old default, you can still set tracker=botsort.yaml explicitly.

• For deployment users ⚙️

  • The revert to the earlier predict() behavior helps avoid export/runtime issues, particularly when using static-batch exported models.
  • This favors compatibility and stability over the recent loader behavior change.

• For solution users 👀

  • Visualization controls are better, and region counting is more accurate frame-to-frame.
  • These fixes reduce confusion and improve reliability in real-world video analytics setups.

• For the broader community 📘

  • Many docs were cleaned up and expanded, making Ultralytics features easier to discover and use.
  • The added ADE20K benchmarks and smaller COCO subsets are especially helpful for quick testing and semantic segmentation evaluation.

In short: v8.4.76 is mainly about making multi-dataset fine-tuning much more practical and scalable 🌟, while also improving default tracking behavior, restoring inference compatibility, and polishing the overall user experience.

What's Changed

• bump ultralytics-inference version to 0.0.22 in documentation by @onuralpszr in #24886
• Add show_boxes argument to instance segmentation solution by @raimbekovm in #24835
• Fix region argument type and default in solutions docs tables by @raimbekovm in #24818
• docs: unified SEO-consistent page titles (≤60, "| Ultralytics" suffix) by @glenn-jocher in #24888
• Fix stale region counts on empty frames in RegionCounter by @raimbekovm in #24819
• Revert "Fix OOM when passing large list of file paths to predict()" by @onuralpszr in #24895
• Use tracktrack as default tracker by @Laughing-q in #24897
• Bump actions/cache from 5 to 6 in /.github/workflows by @dependabot[bot] in #24909
• docs: add ADE20K semantic segmentation benchmarks by @lmycross in #24901
• Add COCO16/COCO32/COCO64 subset datasets to detect docs table by @glenn-jocher in #24912
• docs: add new tracking tips section for OC-SORT, Deep OC-SORT, FastTracker, and TrackTrack by @onuralpszr in #24908
• Restructure five documentation guides and update descriptions by @raimbekovm in #24913
• Skip empty mask filter if native ReID is enabled by @Y-T-G in #24907
• Correct ParkingManagement arguments in parking management guide FAQ by @raimbekovm in #24905
• Add multi-dataset fine-tuning via MultiTrainer by @glenn-jocher in #24914

Full Changelog: v8.4.75...v8.4.76