fpn.pytorch Pytorch implementation of Feature Pyramid Network (FPN) for Object Detection
This project inherits the property of our pytorch implementation of faster r-cnn. Hence, it also has the following unique features:
It is pure Pytorch code. We convert all the numpy implementations to pytorch.
It supports trainig batchsize > 1. We revise all the layers, including dataloader, rpn, roi-pooling, etc., to train with multiple images at each iteration.
It supports multiple GPUs. We use a multiple GPU wrapper (nn.DataParallel here) to make it flexible to use one or more GPUs, as a merit of the above two features.
It supports three pooling methods. We integrate three pooling methods: roi pooing, roi align and roi crop. Besides, we convert them to support multi-image batch training.
We benchmark our code thoroughly on three datasets: pascal voc, coco. Below are the results:
1). PASCAL VOC 2007 (Train/Test: 07trainval/07test, scale=600, ROI Align)
|Res-101||8 TitanX||24||1e-2||10||12||0.22 hr||9688MB||74.2|
Results on coco are on the way.