Weights for Autonomous Campus Navigation

This release provides high-performance pre-trained model weights to support autonomous path tracking research using deep learning. The weights correspond to object detection models trained on the AutoNaVIT dataset and are ideal for benchmarking, transfer learning, and validation tasks in autonomous navigation systems. Key Components: Pre-trained Model Weights Only – No image or annotation files are included in this release Models Provided: YOLOv8n (Nano version) YOLOv8s (Small version) YOLOv5s (Small version) Performance Metrics: All models demonstrated mean Average Precision (mAP) > 95% when evaluated on the full AutoNaVIT dataset Annotation Format Used in Training: CSV format capturing object classification and bounding box coordinates for the following classes: Kerb Obstacle Path These models were trained to recognize key visual cues necessary for autonomous path tracking, such as kerbs, obstacles, and navigable paths. The provided weights are optimized for high accuracy and fast inference, and they can be integrated into diverse object detection pipelines. Note: This release includes only the model weights. The annotated images and label files used for training are part of a separate dataset release.