Meituan YOLOv6 is a cutting-edge object detector that offers remarkable balance between speed and accuracy, making it a popular choice for real-time applications. This model introduces several notable enhancements on its architecture and training scheme, including the implementation of a Bi-directional Concatenation (BiC) module, an anchor-aided training (AAT) strategy, and an improved backbone and neck design for state-of-the-art accuracy on the COCO dataset.

Key Features

  • Bidirectional Concatenation (BiC) Module: YOLOv6 introduces a BiC module in the neck of the detector, enhancing localization signals and delivering performance gains with negligible speed degradation.
  • Anchor-Aided Training (AAT) Strategy: This model proposes AAT to enjoy the benefits of both anchor-based and anchor-free paradigms without compromising inference efficiency.
  • Enhanced Backbone and Neck Design: By deepening YOLOv6 to include another stage in the backbone and neck, this model achieves state-of-the-art performance on the COCO dataset at high-resolution input.
  • Self-Distillation Strategy: A new self-distillation strategy is implemented to boost the performance of smaller models of YOLOv6, enhancing the auxiliary regression branch during training and removing it at inference to avoid a marked speed decline.


You can use YOLOv6 for object detection tasks using the Ultralytics pip package.


Doc: https://docs.ultralytics.com/models/yolov6/

Github: https://github.com/meituan/YOLOv6


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