ultralytics 8.4.52

v8.4.52 - `ultralytics 8.4.52` YOLO Semantic Segmentation models (#24518)

on Python PyPI

🌟 Summary

🚀 Ultralytics 8.4.52 is a major release centered on adding full YOLO26 semantic segmentation support, plus several reliability, export, visualization, and documentation improvements.

📊 Key Changes

• 🆕 New semantic task added across Ultralytics

  • YOLO now supports semantic segmentation as a first-class task alongside detection, instance segmentation, classification, pose, and OBB.
  • This includes support in the CLI, Python API, training, validation, prediction, export, docs, and benchmarks.

• 🧠 New YOLO26 semantic segmentation models

  • Added new pretrained model variants with the -sem suffix, such as:
    • yolo26n-sem.pt
    • yolo26s-sem.pt
    • yolo26m-sem.pt
    • yolo26l-sem.pt
    • yolo26x-sem.pt
  • These models are designed for pixel-level scene understanding, where every pixel is assigned a class.

• 🏗️ Complete semantic segmentation pipeline introduced

  • Added dedicated components for:
    • SemanticSegmentationModel
    • SemanticSegmentationTrainer
    • SemanticSegmentationValidator
    • SemanticSegmentationPredictor
  • This makes semantic segmentation feel like a native YOLO workflow rather than an add-on.

• 🖼️ Semantic masks now appear directly in inference results

  • Added a new SemanticMask result type.
  • Users can now access semantic predictions cleanly in outputs and visualizations.

• 📦 Built-in semantic segmentation datasets added

  • Added dataset configs and docs for:
    • Cityscapes
    • Cityscapes8 for quick testing
    • ADE20K
  • Also added support for PNG mask datasets and even polygon-based semantic training from existing segmentation labels.

• 🔁 Augmentation pipeline upgraded for semantic masks

  • Mosaic, MixUp, CutMix, flipping, perspective transforms, and letterboxing now properly support semantic segmentation masks.
  • This is important for training quality and consistency.

• 📤 Export and deployment support extended

  • Semantic segmentation models now work with export flows and benchmark coverage.
  • Special handling was added for semantic outputs in TensorFlow and multiple export backends.
  • nms=True is automatically disabled for semantic models, since it does not apply to this task.

• 🧪 Much broader testing coverage

  • Added engine, Python, export, benchmark, and dataset tests for the new semantic task.
  • Raspberry Pi skips were added where semantic tests may exceed memory limits.

• 🛠️ Other notable fixes in this release

  • FP16/BF16 FLOPs calculation fixed so half-precision models no longer incorrectly report 0.0 GFLOPs. 📏
  • Final evaluation logging fixed so best metrics no longer overwrite the last epoch in TensorBoard/MLflow. 📈
  • Validation visualizations now respect show_labels and show_conf, making crowded scenes easier to inspect. 👀
  • Dependency handling improved for TensorFlow, Ray Tune, and IMX export by tightening protobuf-related constraints. 📦

🎯 Purpose & Impact

• 🎯 Biggest impact: YOLO26 can now do semantic segmentation natively

  • This opens YOLO to use cases like:
    • autonomous driving
    • road scene understanding
    • medical image parsing
    • land-cover mapping
    • general scene labeling

• ⚡ Easier adoption for existing YOLO users

  • The new task fits into the same familiar YOLO workflow, so users can train and run semantic models with similar commands and APIs they already know.

• 🧩 Broader dataset compatibility

  • Support for both mask-based datasets and polygon labels lowers the barrier to using existing annotations for semantic segmentation projects.

• 📉 More reliable metrics and logging

  • FLOPs reporting is now more accurate for reduced-precision models.
  • Training logs are cleaner and less confusing for experiment tracking tools.

• 🔍 Better debugging and visualization

  • Cleaner validation plots and native semantic result handling make it easier to inspect predictions and analyze model behavior.

• 🚀 Stronger deployment readiness

  • Export, benchmark, and backend updates mean semantic models are not just trainable, but also more practical to validate and deploy.

• 📚 Improved docs and setup guidance

  • New semantic segmentation docs, datasets, CLI references, and install notes make the feature much easier to discover and use.

In short, v8.4.52 is a feature-heavy release led by the launch of YOLO26 semantic segmentation 🎉. For many users, this is the headline change and a meaningful expansion of what Ultralytics can now handle out of the box.

What's Changed

• Fix get_flops dtype mismatch on half-precision models by @ahmet-f-gumustas in #24504
• Add https://youtu.be/Jj7WpfiegD0 & https://youtu.be/Kpl6cOUKbb8 to docs by @RizwanMunawar in #24514
• fix: 🐞 add googleapis-common-protos dependency to resolve protobuf compatibility issues with TensorFlow and raytune by @onuralpszr in #24509
• docs: 📝 add installation instructions for NVIDIA DALI with CUDA 13.x by @onuralpszr in #24498
• Update OpenVINO YOLO26 benchmarks with Intel® Core™ Ultra™ X7 358H by @lakshanthad in #24512
• Fix custom get_model signature in usage/engine.md by @raimbekovm in #24531
• fix: on_fit_epoch_end in final_eval overwriting last epoch metrics by @deependujha in #24530
• Fix unrendered links in YOLOv5 compatibility warning by @raimbekovm in #24521
• feat: hide labels and conf in visualizations plotting by @K-saif in #24305
• Fix docs links with trailing slash with latest zensical 0.0.43 by @Laughing-q in #24533
• docs: update enterprise license description by @raimbekovm in #24473
• Bump idna from 3.10 to 3.15 in /examples/RTDETR-ONNXRuntime-Python in the pip group across 1 directory by @dependabot[bot] in #24540
• ultralytics 8.4.52 YOLO Semantic Segmentation models by @Laughing-q in #24518

New Contributors

• @deependujha made their first contribution in #24530

Full Changelog: v8.4.51...v8.4.52