ultralytics 8.3.119

v8.3.119 - `ultralytics 8.3.119` New CutMix image augmentation (#19870)

on Python PyPI

🌟 Summary

Ultralytics 8.3.119 introduces the powerful CutMix image augmentation technique, streamlines Docker and export processes, and enhances dependency and logging support for a smoother user experience. 🖼️🔧🚀

📊 Key Changes

• CutMix Data Augmentation Added 🎨

  • New CutMix augmentation randomly combines regions from two images, improving model robustness to occlusions.
  • Fully supports both object detection and classification tasks.
  • Configurable via new cutmix hyperparameter (probability) and beta (mixing ratio).

• Docker and Dependency Improvements 🐳

  • Dockerfiles cleaned up: unnecessary packages (g++, libusb-1.0-0, keras) removed for smaller, faster images.
  • Docker Quickstart documentation made clearer and less redundant.

• Export and Logging Enhancements 📦

  • ONNXSlim dependency updated for more reliable ONNX and TensorFlow exports.
  • TFLite export process simplified—metadata now added in a more robust, Python-version-friendly way.
  • Default logging integrations switched to Weights & Biases (wandb) and MLflow for better experiment tracking.

• Documentation & Usability Updates 📚

  • CutMix fully documented in augmentation guides and hyperparameter tables.
  • Label format descriptions clarified for developers.
  • New contributor recognized in documentation.

🎯 Purpose & Impact

• Better Model Generalization: CutMix creates realistic occlusion scenarios, helping models learn to detect and classify objects even when partially hidden—especially valuable for real-world applications like autonomous driving or crowded scenes.
• Easier Experimentation: Users can now tune CutMix alongside other augmentations, both via config files and hyperparameter search tools like Ray Tune.
• Smoother Setup & Export: Docker images are now lighter and faster to build, and model export is more reliable across platforms and Python versions.
• Improved Logging & Compatibility: Enhanced logging options and relaxed dependency constraints mean fewer installation issues and better tracking of training runs.
• Clearer Documentation: Updated guides and parameter tables make it easier for both new and experienced users to leverage the latest features.

Overall, this release empowers users to build more robust, production-ready models with less friction and more flexibility! 🚀

What's Changed

• Update onnxslim>=0.1.46 by @glenn-jocher in #20369
• Add W&B and MLFlow to pyproject.toml logging dependencies by @glenn-jocher in #20371
• Fix NMS box format comment from xyxy to xywh by @nikenake1 in #20376
• Update docker-quickstart.md by @SHOscarChen in #20380
• Remove tflite_support and flatbuffers dependencies by @glenn-jocher in #20383
• Dockerfile cleanup by @glenn-jocher in #20382
• Remove g++ and libusb-1.0-0 from Docker linux installs by @glenn-jocher in #20384
• ultralytics 8.3.119 New CutMix image augmentation by @artzuros in #19870

New Contributors

• @nikenake1 made their first contribution in #20376
• @SHOscarChen made their first contribution in #20380
• @artzuros made their first contribution in #19870

Full Changelog: v8.3.118...v8.3.119