Semi Supervised Object Detection With Pseudo Labeling And Consistency Regularization

via “semi-supervised and self-supervised learning with pseudo-labeling”

OpenMMLab detection toolbox with 300+ models.

Unique: Implements semi-supervised detection with pseudo-labeling where a teacher model generates labels on unlabeled data, and a student model is trained with both labeled and pseudo-labeled data; uses exponential moving average (EMA) teacher updates for stability and consistency regularization for improved robustness

vs others: More practical than fully self-supervised approaches because it leverages labeled data when available; more stable than naive pseudo-labeling because EMA teacher updates reduce label noise; better integrated than external semi-supervised frameworks because it's built into the training pipeline