A Large-scale Evaluation of Pretraining Paradigms for the Detection of
  Defects in Electroluminescence Solar Cell Images
David Torpey Lawrence Pratt Richard Klein
Pretraining has been shown to improve performance in many domains, including
semantic segmentation, especially in domains with limited labelled data. In
this work, we perform a large-scale evaluation and benchmarking of various
pretraining methods for Solar Cell Defect Detection (SCDD) in
electroluminescence images, a field with limited labelled datasets. We cover
supervised training with semantic segmentation, semi-supervised learning, and
two self-supervised techniques. We also experiment with both in-distribution
and out-of-distribution (OOD) pretraining and observe how this affects
downstream performance. The results suggest that supervised training on a large
OOD dataset (COCO), self-supervised pretraining on a large OOD dataset
(ImageNet), and semi-supervised pretraining (CCT) all yield statistically
equivalent performance for mean Intersection over Union (mIoU). We achieve a
new state-of-the-art for SCDD and demonstrate that certain pretraining schemes
result in superior performance on underrepresented classes. Additionally, we
provide a large-scale unlabelled EL image dataset of $22000$ images, and a
$642$-image labelled semantic segmentation EL dataset, for further research in
developing self- and semi-supervised training techniques in this domain.