How to Train YOLO-NAS on a Custom Dataset

YOLO-NAS, developed by Deci, is a cutting-edge real-time object detection model that surpasses its predecessors like YOLOv6 and YOLOv8 in terms of mean average precision (mAP) and inference speed on datasets such as COCO and Roboflow 100. The guide outlines the process of training this model on custom datasets using the super-gradients Python package, detailing steps such as loading pre-trained models, setting hyperparameters, and evaluating outcomes. YOLO-NAS is notable for its ease of fine-tuning, facilitated by tools like Autodistill, which simplifies model training with minimal code and data labeling. The guide also emphasizes the importance of setting the appropriate model size, batch size, and number of epochs for effective training, and provides insights into using software tools like TensorBoard and experiment loggers for tracking training metrics.