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:
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It is pure Pytorch code. We convert all the numpy implementations to pytorch.
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It supports trainig batchsize > 1. We revise all the layers, including dataloader, rpn, roi-pooling, etc., to train with multiple images at each iteration.
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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.
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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 Â | 8 TitanX | 24 | 1e-2 | 10 | 12 | 0.22 hr | 9688MB | 74.2 |
Results on coco are on the way.