FsDet provides implementations for few-shot object detection benchmarks on PASCAL VOC, COCO, and LVIS datasets. The repository includes a two-stage fine-tuning approach where an object detector is first trained on base classes with abundant data, then only the last layers are fine-tuned on a small balanced dataset of novel classes. It offers benchmark results, pre-trained models, and modularized code for adding custom datasets and models.