Neural Architecture Search in Roboflow Train
Neural Architecture Search (NAS) is now available as a training engine for object detection and instance segmentation projects. Instead of manually tuning hyperparameters and running multiple separate training jobs, NAS automatically evaluates thousands of candidate architectures in a single run. This allows you to find the optimal balance of inference speed and accuracy tailored to your specific dataset and deployment hardware.
Using a unique weight-sharing strategy, NAS explores these configurations simultaneously at a fraction of the traditional compute cost. Once training is complete, you can evaluate the resulting models on a speed-vs-accuracy graph to deploy the version that best fits your performance requirements, often achieving better latency and accuracy than standard fine-tuning.
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