YOLOv12 training and deployment capability
YOLOv12 provides a complete model development workflow that supports end-to-end training with user-defined datasets. The system requires datasets to follow the YOLO standard format, contain images and labels directory structure, and configure data paths and category information through the data.yaml file. A typical training process involves more than 250 epochs of iterations, and the input image size is 640×640 pixels by default, and all these parameters can be adjusted according to actual needs.
In terms of model deployment, YOLOv12 supports export to two mainstream inference formats, ONNX and TensorRT, and provides FP16 semi-precision export option especially for edge computing devices, which can significantly improve the computational efficiency of the deployment environment. The project also has a built-in supervision visualization toolkit, which facilitates developers to monitor the dynamic changes of loss curves and validation set metrics during the training process, as well as to visually present the prediction results. This complete development ecosystem makes the transformation of YOLOv12 from experiment to production efficient and reliable.
This answer comes from the articleYOLOv12: Open source tool for real-time image and video target detectionThe
