TopNet-Object-Placement

An implementation of the paper "TopNet: Transformer-based Object Placement Network for Image Compositing", CVPR 2023.

Setup

All the code have been tested on PyTorch 1.7.0. Follow the instructions to run the project.

First, clone the repository:

git clone git@github.com:bcmi/TopNet-Object-Placement.git

Then, install Anaconda and create a virtual environment:

conda create -n TopNet
conda activate TopNet

Install PyTorch 1.7.0 (higher version should be fine):

conda install pytorch==1.7.0 torchvision==0.8.0 torchaudio==0.7.0 cudatoolkit=10.2 -c pytorch

Install necessary packages:

pip install -r requirements.txt

Data Preparation

Download and extract data from Baidu Cloud(access code: 4zf9) or Google Drive. Put them in "data/data". It should contain the following directories and files:

<data/data>
  bg/                         # background images
  fg/                         # foreground images
  mask/                       # foreground masks
  train(test)_pair_new.json   # json annotations 
  train(test)_pair_new.csv    # csv files

Training

Before training, modify "config.py" according to your need. After that, run:

python train.py

Test

To get the F1 score and balanced accuracy of a specified model, run:

python test.py --load_path <PATH_TO_MODEL> 

Evalution on Discriminative Task

Evalution on Generation Task