YOLO12 introduces an attention-centric architecture that departs from the traditional CNN-based approaches used in previous YOLO models, yet retains the real-time inference speed essential for many applications. This model achieves state-of-the-art object detection accuracy through novel methodological innovations in attention mechanisms and overall network architecture, while maintaining real-time performance.
<strong>Watch:</strong> How to Use YOLO12 for Object Detection with the Ultralytics Package | Is YOLO12 Fast or Slow? 🚀
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## Key Features
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- **Enhanced Efficiency**: Achieves higher accuracy with fewer parameters compared to many prior models, demonstrating an improved balance between speed and accuracy.
- **Flexible Deployment**: Designed for deployment across diverse platforms, from edge devices to cloud infrastructure.
YOLO12 supports a variety of computer vision tasks. The table below shows task support and the operational modes (Inference, Validation, Training, and Export) enabled for each:
Muhammad Rizwan Munawar
3 weeks ago
committed by
GitHub