Model weights and data for paper "Self-Supervised Learning Approach for Multi-label Sewer Defect Classification"

<strong>Repository for model weights and data</strong> accompanying the paper <strong>“Self-Supervised Learning Approach for Multi-label Sewer Defect Classification”</strong> by <strong>Tugba Yildizli, Tianlong Jia, Jeroen Langeveld, and Riccardo Taormina</strong>.<br>This repository provides:<strong>SwAV pre-trained weights</strong> for self-supervision,<strong>Fine-tuned model weights</strong> (fully supervised and semi-supervised),<strong>Supporting data/configs</strong> used to train and analyze these models.<br>Researchers can (i) <strong>fine-tune</strong> the SwAV pre-trained backbones on their own sewer datasets for semi-supervised learning, and (ii) <strong>evaluate</strong> our fine-tuned models for reproducibility. All code examples use <strong>PyTorch</strong>.