Researchers from Waabi and the University of Toronto Introduce LabelFormer: An Efficient Transformer-Based AI Model to Refine Object Trajectories for Auto-Labelling

Modern self-driving systems frequently use Large-scale manually annotated datasets to train object detectors to recognize the traffic participants in the picture. Auto-labeling methods that automatically produce sensor data labels have recently gained more attention. Auto-labeling may provide far bigger datasets at a fraction of the expense of human annotation if its computational cost is less […]

The post Researchers from Waabi and the University of Toronto Introduce LabelFormer: An Efficient Transformer-Based AI Model to Refine Object Trajectories for Auto-Labelling appeared …

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