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README.md 14.44 KiB

LOST

ECE613 README

Convert KITTI Labels to COCO version

cd tools/Kitti2Coco
python kitti2coco-label-trans.py train $KITTI_ROOT
python kitti2coco-label-trans.py val $KITTI_ROOT
cd -

pip install -r requirements.txt
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 -

# replace $KITTI_ROOT with path to kitti
# replace * with the script you want to run
KITTI_ROOT=$KITTI_ROOT ./scripts/run_*.sh

Original README

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

Localizing Objects with Self-Supervised Transformers and no Labels, BMVC 2021 [arXiv]
by Oriane Siméoni, Gilles Puy, Huy V. Vo, Simon Roburin, Spyros Gidaris, Andrei Bursuc, Patrick Pérez, Renaud Marlet and Jean Ponce

LOST visualizations LOST visualizations LOST visualizations


If you use the LOST code or framework in your research, please consider citing:

@inproceedings{LOST,
   title = {Localizing Objects with Self-Supervised Transformers and no Labels},
   author = {Oriane Sim\'eoni and Gilles Puy and Huy V. Vo and Simon Roburin and Spyros Gidaris and Andrei Bursuc and Patrick P\'erez and Renaud Marlet and Jean Ponce},
   journal = {Proceedings of the British Machine Vision Conference (BMVC)},
   month = {November},
   year = {2021}
}

Content

LOST

Towards unsupervised object detection

Installation of LOST

Dependencies

This code was implemented with python 3.7, PyTorch 1.7.1 and CUDA 10.2. Please install PyTorch. In order to install the additionnal dependencies, please launch the following command:

pip install -r requirements.txt

Install DINO

This method is based on 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 ../;

The code was made using the commit ba9edd1 of DINO repo (please rebase if breakage).

Apply LOST to one image

Following are scripts to apply LOST to an image defined via the image_path parameter and visualize the predictions (pred), the maps of the Figure 2 in the paper (fms) and the visulization of the seed expansion (seed_expansion). Box predictions are also stored in the output directory given by parameter output_dir.

python main_lost.py --image_path examples/VOC07_000236.jpg --visualize pred
python main_lost.py --image_path examples/VOC07_000236.jpg --visualize fms
python main_lost.py --image_path examples/VOC07_000236.jpg --visualize seed_expansion

Launching LOST on datasets

Following are the different steps to reproduce the results of LOST presented in the paper.

PASCAL-VOC

Please download the PASCAL VOC07 and PASCAL VOC12 datasets (link) and put the data in the folder datasets. There should be the two subfolders: datasets/VOC2007 and datasets/VOC2012. In order to apply lost and compute corloc results (VOC07 61.9, VOC12 64.0), please launch: