How to Train a YOLOv6 Model on a Custom Dataset

YOLOv6, a recent addition to the YOLO (You Only Look Once) family of models, was released by Meituan in June 2022, quickly gaining popularity with over 2,000 stars and 300 forks on GitHub. Designed for state-of-the-art performance on the COCO dataset benchmark, YOLOv6 introduces innovations such as an efficient decoupled head with SIoU loss and a hardware-friendly design for its Backbone and Neck. The model is available in three sizes—nano, tiny, and small—with plans for larger versions. An essential aspect of training YOLOv6 is preparing a high-quality dataset, and tools like Roboflow can assist in managing datasets, labeling data, and converting annotations to the YOLOv6 format. The training process involves setting up a development environment with dependencies, configuring custom training options, and evaluating model performance through metrics like mean average precision (mAP). YOLOv6 can also be converted to ONNX for better portability and integrated with active learning strategies to improve model performance.