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

Repository for model weights and data accompanying the paper “Self-Supervised Learning Approach for Multi-label Sewer Defect Classification” by Tugba Yildizli, Tianlong Jia, Jeroen Langeveld, and Riccardo Taormina.

This repository provides:
SwAV pre-trained weights for self-supervision,
Fine-tuned model weights (fully supervised and semi-supervised),
Supporting data/configs used to train and analyze these models.

Researchers can (i) fine-tune the SwAV pre-trained backbones on their own sewer datasets for semi-supervised learning, and (ii) evaluate our fine-tuned models for reproducibility. All code examples use PyTorch.