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A higher performance PyTorch implementation of Single-Shot Refinement Neural Network for Object Detection. The official and original Caffe code can be found here.

Table of Contents

       

Performance

VOC2007 Test

mAP (Single Scale Test)
ArchPaperCaffe VersionOur PyTorch Version
RefineDet32080.0%79.52%79.81%
RefineDet51281.8%81.85%80.50%

Installation

Datasets

To make things easy, we provide bash scripts to handle the dataset downloads and setup for you. We also provide simple dataset loaders that inherit torch.utils.data.Dataset, making them fully compatible with the torchvision.datasets API.

COCO

Microsoft COCO: Common Objects in Context

Download COCO 2014
# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/COCO2014.sh

VOC Dataset

PASCAL VOC: Visual Object Classes

Download VOC2007 trainval & test
# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2007.sh # <directory>
Download VOC2012 trainval
# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2012.sh # <directory>

Training RefineDet

mkdir weights
cd weights
wget https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
./train_refinedet320.sh  #./train_refinedet512.sh

Evaluation

To evaluate a trained network:

./eval_refinedet.sh

You can specify the parameters listed in the eval_refinedet.py file by flagging them or manually changing them.

TODO

We have accumulated the following to-do list, which we hope to complete in the near future

References