Fpn.pytorch

fpn.pytorch Pytorch implementation of Feature Pyramid Network (FPN) for Object Detection

Introduction

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.

Benchmarking

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)

model | GPUs | Batch Size | lr | lr_decay | max_epoch | Speed/epoch | Memory/GPU | mAP ---------|-----------|----|-----------|-----|-----|-------|--------|-------- Res-101   | 8 TitanX | 24| 1e-2 | 10 | 12 | 0.22 hr | 9688MB | 74.2

Results on coco are on the way.