Launch: Use YOLOv12 with Roboflow

YOLOv12, a newly released object detection model as of February 18th, 2025, offers improved accuracy and reduced latency compared to its predecessors when tested on the Microsoft COCO dataset. The model can now be trained and deployed on the Roboflow platform, which supports data labeling, training, and deployment processes, including serverless API deployment options. Users can label their data using the YOLOv8 PyTorch TXT format and utilize Roboflow's annotation tools to expedite the process. The training options on Roboflow allow for selecting model types based on speed and accuracy needs, with the possibility of using a pre-trained YOLOv12 COCO Checkpoint for enhanced accuracy. Deployment can occur on various hardware, including devices with CUDA-capable GPUs, using Roboflow's serverless API or Python SDK. This guide also provides a step-by-step walkthrough for labeling, training, and deploying YOLOv12 models, emphasizing the ease of integration with Roboflow's tools and services.