CutLER is an approach for training object detection and instance segmentation models without human annotations by leveraging cut-and-paste techniques and self-supervised learning. The project includes VideoCutLER, which extends the approach to video instance segmentation. It provides pretrained models, training code, and evaluation pipelines for unsupervised detection tasks across multiple benchmarks.