MVP—A Multistep Visual Pretraining Pipeline for Efficient Weather Recognition

Automatic weather recognition using images has important applications in land and air traffic control, autonomous vehicles, road safety, and crop management. Despite its relevance, this field remains underexplored due to the difficulty of extracting robust features for weather conditions that often overlap visually, and the high cost of building large, labeled datasets. To address these challenges, we propose a Multistep Visual Pretraining (MVP) approach, a resource-efficient training pipeline designed to maximize performance with limited supervision. MVP leverages self-supervised pretraining on a large-scale unlabeled weather dataset, followed by supervised fine-tuning on a small labeled dataset, while reusing ImageNet pretrained models to minimize computational requirements. Unlike traditional strategies that require massive amounts of manually labeled data and extensive GPU resources, MVP achieves competitive performance with a compact ResNet-50 architecture, only 1200 labeled images for fine-tuning, and a single mid-range GPU. Our method reached 94% accuracy in multi-class weather recognition, demonstrating that robust, real-world performance can be achieved without the prohibitive costs of large-scale annotation or high-end computing infrastructure. This study extends our previous work, “Weather Recognition Using Self-Supervised Deep Learning”, by moving from binary to multi-class classification while reducing data and resource dependencies, thereby offering a scalable and practical solution for real-world deployment.