Ultralytics YOLO

🌟 Summary

This release introduces full support for multispectral (multi-channel) images in the Ultralytics ecosystem, allowing YOLO models to train, validate, predict, and export with images containing more than 3 channels (e.g., 10-channel multispectral data). 🌈

📊 Key Changes

• Multispectral Image Support:

  • Added robust handling for images with any number of channels (not just RGB) across all YOLO tasks (detection, segmentation, pose, classification, etc.).
  • Introduced a channels field in dataset configuration files to specify the number of image channels.
  • Updated data loaders, caching, and image reading utilities to correctly process multi-channel images, including TIFF support.
  • All model initialization, training, validation, and export routines now dynamically adapt to the dataset's channel count.

• New COCO8-Multispectral Dataset:

  • Added a 10-channel multispectral version of the COCO8 dataset for rapid testing and experimentation.
  • Provided a utility to convert standard RGB images to multispectral format via interpolation.
  • Comprehensive documentation and usage examples for multispectral datasets.

• Augmentation & Preprocessing Improvements:

  • Data augmentations and transformations now intelligently apply only to compatible channel configurations.
  • Enhanced plotting and visualization to handle multi-channel data gracefully.

• Other Notable Updates:

  • Improved MobileSAM documentation for clarity and easier comparison with YOLO models.
  • Updated callbacks documentation with a new, more relevant YouTube tutorial.
  • Enhanced dataset splitting utilities and documentation for classification tasks.
  • Standardized logging for warnings and errors, making messages clearer and more consistent.
  • Improved test coverage for ARM64 systems and multispectral workflows.

🎯 Purpose & Impact

• Unlocks Advanced Use Cases:

  • Enables researchers and practitioners to work with multispectral and hyperspectral imagery (e.g., satellite, medical, or scientific images) directly in Ultralytics and YOLO models.
  • Facilitates new applications in agriculture, remote sensing, and any domain requiring spectral analysis beyond standard RGB.

• Seamless Integration:

  • Multispectral support is built-in and automatic—no need for custom code or workarounds.
  • All core YOLO workflows (training, validation, prediction, export) now support multi-channel data out of the box.

• Enhanced Experimentation:

  • The new COCO8-Multispectral dataset provides a quick, lightweight way to test multispectral pipelines and debug models.
  • Utility functions make it easy to convert existing datasets to multispectral format for experimentation.

• Improved Usability & Documentation:

  • Clearer docs, better logging, and more robust dataset handling make the platform easier to use for both new and advanced users.
  • ARM64 and cross-platform improvements ensure broader compatibility.

In summary:
This update is a major step forward for users needing advanced image analysis, making Ultralytics and YOLO models more versatile and ready for real-world, multi-channel data challenges. 🚀

What's Changed

• Update MobileSAM documentation by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20219
• Add https://youtu.be/ENQXiK7HF5o to docs by @RizwanMunawar in https://github.com/ultralytics/ultralytics/pull/20218
• New COCO8-Multispectral dataset by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20221
• Add Tests CI after building Arm64 Docker images by @lakshanthad in https://github.com/ultralytics/ultralytics/pull/19672
• RTDETR: Remove unused preprocess_batch function by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20215
• Fix stale workflow by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/20227
• Update extra.js with 0.3.20/dist/embed.min.js by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20230
• Allow validation with rect for dynamic models by @Y-T-G in https://github.com/ultralytics/ultralytics/pull/20232
• Scope tensorboard.SummaryWriter import by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20220
• Fix test_solutions.py Streamlit test by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20233
• Classify results background transparency by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20239
• Fix Annotator tuples>lists by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20242
• Add PIL results.plot(save=True) test by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20241
• Refactor autosplit and implement split_classify_dataset by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20245
• Integrate "WARNING ⚠️" and "ERROR ❌" prefixes for LOGGER by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20246
• Update build_reference.py for automatic mkdocs.yml updates by @glenn-jocher in https://github.com/ultralytics/ultralytics/pull/20247
• ultralytics 8.3.12 New YOLO Multispectral Image Support by @Laughing-q in https://github.com/ultralytics/ultralytics/pull/20223

Full Changelog: https://github.com/ultralytics/ultralytics/compare/v8.3.111...v8.3.112