Launch: Train and Deploy YOLO-NAS Models on Roboflow

YOLO-NAS is an open-source object detection model developed by Deci AI, leveraging the YOLO architecture and employing Neural Architecture Search (NAS) to optimize its performance. This model, known for its lower latency and higher accuracy compared to predecessors like YOLOv6, YOLOv7, and YOLOv8, can now be trained and deployed on the Roboflow platform. Users can prepare datasets in Roboflow, select the YOLO-NAS training option, and monitor training progress. Once trained, the model can be tested and deployed using Roboflow Inference, an open-source tool that allows for deployment on personal hardware or through the Roboflow hosted API. The guide walks through the process of setting up, training, and deploying a YOLO-NAS model, including the use of InferencePipeline to deploy models on webcams, highlighting the practical application and flexibility of the model in real-time scenarios.

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