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

DOI:10.4121/1c21ce33-715f-4ca0-89fa-c170b30801ff.v2
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/1c21ce33-715f-4ca0-89fa-c170b30801ff

Datacite citation style

Yıldızlı, Tuğba; Jia, Tianlong; Langeveld, Jeroen; Taormina, Riccardo (2025): Model weights and data for paper "Self-Supervised Learning Approach for Multi-label Sewer Defect Classification". Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/1c21ce33-715f-4ca0-89fa-c170b30801ff.v2
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

Version 2 - 2025-11-11 (latest)
Version 1 - 2025-11-04

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.

History

  • 2025-11-04 first online
  • 2025-11-11 published, posted

Publisher

4TU.ResearchData

Format

.torch files for SwAV pre-training weights, .pth files fine-tuned model weigths, .txt files for image names, .csv files for data annotation

Organizations

TU Delft, Faculty of Civil Engineering and Geosciences, Department of Water Management

DATA

Files (2)