YOLOv8-multi-task
The project presents a streamlined model for integrating three tasks into a single framework, emphasizing efficient real-time multi-task learning. It features an Adaptive Concatenate Module designed for segmentation and a universal segmentation head, providing notable performance in practical scenarios. Through extensive testing, the model demonstrates significant improvements over existing methods in terms of inference speed and visualization, using publicly available autonomous driving datasets and actual road data. The solution is implemented with Python and PyTorch, providing clear guidance for training, evaluation, and prediction, making it a practical choice for enhancing complex autonomous driving functions.
