ultralytics 8.3.78

v8.3.78 - `ultralytics 8.3.78` new YOLO12 models (#19325)

on Python PyPI

🌟 Summary

This release, v8.3.78, brings an exciting new model to the family: YOLO12 🚀, featuring an attention-centric design for superior accuracy and efficiency across a variety of computer vision tasks.

📊 Key Changes

• Introduction of YOLO12 Models:

  • New Architecture: Incorporates attention mechanisms like Area Attention, R-ELAN, and FlashAttention for optimized performance.
  • Comprehensive Task Support:
    • Object detection, segmentation, pose estimation, classification, and oriented bounding box (OBB) detection.
  • Benchmark Improvements:
    • Demonstrates higher mAP (mean Average Precision) and efficiency compared to YOLO10/YOLO11 and competitors like RT-DETR.

• Model-Specific Enhancements:

  • Introduced multiple YOLO12 variants (n, s, m, l, x) catering to different computing environments such as cloud systems and edge devices.
  • Added new task-focused configurations for image classification, pose estimation, and segmentation.

• Documentation Updates:

  • YOLO12 now included in detailed model documentation with performance metrics and usage examples.
  • Extensive references, including benchmarks for comparison with leading global detection models.

• Code Simplifications and Bug Fixes:

  • ONNX Run-Time Fixes: Improved device handling and tensor reshaping for ONNX users.
  • TFLite Export Cleanup: Removed redundant parameters for simpler TensorFlow Lite export logic.
  • Code Refinement: Enhanced readability and maintainability across inference and export pipelines.

🎯 Purpose & Impact

• Purpose:

  • YOLO12 brings a paradigm shift in accuracy and efficiency by adopting attention mechanisms tailored for real-time object detection.
  • Streamlines codebase for easier maintenance and integration in diverse projects.

• Impact:

  • Developers gain access to cutting-edge state-of-the-art models excelling in versatility, speed, and precision.
  • Tasks like multi-object detection, segmentation, and pose estimation become more accessible for smaller devices (e.g., edge devices).
  • Improved user experience with easier model selection, robust export support, and refined prediction outputs.

🔮 This update is not only a leap forward in technological advancement but also a commitment to making intelligent vision accessible to all.

What's Changed

• Remove unused parameters from export_tflite by @Y-T-G in #19319
• Fix ONNX RuntimeError with dynamic WorldModel by @Y-T-G in #19322
• Add YOLO12 model info by @Laughing-q in #19328
• Add https://youtu.be/BPYkGt3odNk to docs by @RizwanMunawar in #19331
• Refactor with simplifications by @glenn-jocher in #19329
• update model_data.py by @RizwanMunawar in #19330
• ultralytics 8.3.78 new YOLO12 models by @Laughing-q in #19325

Full Changelog: v8.3.77...v8.3.78