Detecting rare objects with a few examples is an emerging problem. Prior works show meta-learning is a promising approach. But, fine-tuning techniques have drawn scant attention. We find that fine-tuning only the last layer of existing detectors on rare classes is crucial to the few-shot object detection task. Such a simple approach outperforms the meta-learning methods by a large margin on current benchmarks and sometimes even doubles the accuracy of the prior methods. However, the high varianc…