# MetaDet - introduces a weight prediction model to predict the model parameters from its few-shot parameters. - the meta-learning strategy is designed to disentangle the learning of class-agnostic and class-specific weight parameters. - this work focuses on **weight parameter generation/prediction** for the detector network (box head) by learning a meta-model that regresses the class-specific parameters trained from few-shot to class-specific parameters trained from large base classes. ![](https://i.imgur.com/GNkmJlX.png) ## Training - in the first stage, a detector is trained on large base classes, specifically to learn the class-agnostic parameters. - in the second stage, only the class-specific parameters are learned while the class-agnostic parameters are fixed. The meta model T is trained by receiving the class-specific weights in the last layer and class-specific weights from few-shot training as inputs, where T is parameterized by the class-agnostic weights. - Loss function for training T is: ![](https://i.imgur.com/mRxybqS.png)