FOUND

Pytorch implementation of the unsupervised object localization method FOUND. More details can be found in the paper:

Unsupervised Object Localization: Observing the Background to Discover Objects, arxiv 2022 [arXiv]
by Oriane Siméoni, Chloé Sekkat, Gilles Puy, Antonin Vobecky, Eloi Zablocki and Patrick Pérez

If you use our FOUND code or framework in your research, please consider citing:

@inproceedings{simeoni2022unsupervised,
   title = {Unsupervised Object Localization: Observing the Background to Discover Objects},
   author = {Oriane Sim\'eoni and  Chlo\'e Sekkat and Gilles Puy and Antonin Vobecky and Eloi Zablocki and Patrick P\'erez},
   journal = {},
   month = {Decembre},
   year = {2022}
}

Installation of FOUND

Environment installation

This code was implemented with python 3.7, PyTorch 1.8.1 and CUDA 11.1. Please install PyTorch. In order to install the additionnal dependencies, please launch the following command:

# Create conda environment
conda create -n found python=3.7

# Install dependencies
conda activate found
pip install -r requirements.txt

Please install also DINO paper. The framework can be installed using the following commands:

git clone https://github.com/facebookresearch/dino.git
cd dino; 
touch __init__.py
echo -e "import sys\nfrom os.path import dirname, join\nsys.path.insert(0, join(dirname(__file__), '.'))" >> __init__.py; cd ../;

Usage of FOUND

We provide here the different command lines in order to repeat all results provided in our paper.

Application to one image

Using the following command it is possible to apply our method to one image

python main_visualize.py --img-path /datasets_local/VOC2007/JPEGImages/000030.jpg

Saliency object detection

We evaluate our method FOUND for the saliency detection on the datasets DUT-OMRON, DUTS-TEST, ECSSD. Please download those dataset from http://saliencydetection.net/dut-omron/, http://saliencydetection.net/duts/ and https://www.cse.cuhk.edu.hk/leojia/projects/hsaliency/dataset.html respectively. The parameter --evaluation-mode allow to choose between single and multi setup and --apply-bilateral can be added to apply the bilateral solver. Please find here examples on the dataset ECSSD.

python main_found_evaluate.py --eval-type saliency --dataset-eval ECSSD --evaluation-mode single --apply-bilateral
python main_found_evaluate.py --eval-type saliency --dataset-eval ECSSD --evaluation-mode multi --apply-bilateral