phyllish/pmfnet

Implementation of "Pose-aware Multi-level Feature Network for Human Object Interaction Detection"(ICCV 2019 Oral)

Pose-aware Multi-level Feature Network for Human Object Interaction Detection

Official implementation of "Pose-aware Multi-level Feature Network for Human Object Interaction Detection"(ICCV 2019 Oral).

This code follows the implementation architecture of roytseng-tw/mask-rcnn.pytorch.

Getting Started

Requirements

Tested under python3.

• python packages
  • pytorch==0.4.1
  • torchvision==0.2.2
  • pyyaml==3.12
  • cython
  • matplotlib
  • numpy
  • scipy
  • opencv
  • packaging
  • ipdb
  • pycocotools — for COCO dataset, also available from pip.
  • tensorboardX — for logging the losses in Tensorboard
• An NVIDAI GPU and CUDA 8.0 or higher. Some operations only have gpu implementation.

Assume the project is located at $ROOT.

Compilation

Compile the NMS code:

cd $ROOT/lib 
sh make.sh

Data and Pretrained Model Preparation

Create a data folder under the repo,

cd $ROOT
mkdir data

• COCO: Download the coco images and annotations from coco website.

  Our data: Download the our dataset annotations and detection/keypoint proposals from our data.

  Pose estimatiotn We use the repo pytorch-cpn to train our pose estimator. We have released our keypoint predictions of vcoco dataset on our data.

  And make sure to put the files as the following structure:

  data
  ├───coco
  │   ├─images
  │   │  ├─train2014
  │   │  ├─val2014 
  │   │
  │   ├─vcoco
  │      ├─annotations
  │      ├─annotations_with_keypoints
  │      ├─vcoco
  │
  ├───cache
  │   ├─addPredPose
  │
  ├───pretrained_model
      ├─e2e_faster_rcnn_R-50-FPN_1x_step119999.pth
      ├─vcoco_best_model_on_test.pth
  
  

Training

cd $ROOT
sh script/train_vcoco.sh

Test

cd $ROOT
sh script/test_vcoco.sh

Our pretrained model vcoco_best_model_on_test.pth has 52.05 AP on vcoco test set.