PFPNet.pytorch

This repository provides the official PyTorch implementation for paper: Parallel Feature Pyramid Network for Object Detection by Seung-Wook Kim.

Note: PFPNet is originally implemented on Caffe. Following Caffe version, we re-implemented Pytorch version.

Installation

• Install PyTorch by selecting your environment on the website and running the appropriate command. Note: We experiment on Pytorch 1.4

• Clone this repository.

• Then download the dataset by following the instructions below.

• We now support Tensorboard for real-time loss visualization and validation during training!

Datasets

Currently, we only provide PFPNet of Pascal VOC version.

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

• First download the fc-reduced VGG-16 PyTorch base network weights at: https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
• By default, we assume you have downloaded the file in the PFPNet.pytorch/weights dir:

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

• Use the following script below to train network .

python main.py --mode 'train' --dataset 'VOC' --save_folder 'weights/' --basenet './weights/vgg16_reducedfc.pth'

• Note:
  • For training, an NVIDIA GPU is strongly recommended for speed.
  • For instructions on Tensorboard usage/installation, see the Installation section.
  • You can pick-up training from a checkpoint by specifying the path as one of the training parameters (again, see main.py for options)

Note: COCO version and PFPNet512 are unavailable.

Evaluation

To evaluate a trained network:

python main.py --mode 'test' --dataset 'VOC' --save_folder 'weights/' --test_model 'weights/PFPNetR320.pkl'

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

Performance

VOC2007

mAP

PFP320	Paper version (Implemented by Caffe)	Pytorch version
mAP	80.7	80.7
FPS	33	41

PFPNetR320: https://drive.google.com/file/d/1xEcdMGgmPNyopeNHEhTWQAbjFhl1LHAY/view?usp=sharing

References

• Original Implementation (CAFFE)
• A list of other great SSD ports that were sources of inspiration:
  • amdegroot/ssd.pytorch
  • lzx1413/PytorchSSD
  • luuuyi/RefineDet.PyTorch