MEPU-OWOD

Code Implementation of "Unsupervised Recognition of Unknown Objects for Open-World Object Detection" (arXiv 2308.16527)

Introduction

Our Key Technique: Reconstruction Error-based Weibull modeling (REW)

Update

The pre-trained SOCO backbone, pre-computed proposals and trained weight of MEPU can be downloaded from GoogleDrive.

Dataset Preparation

Please first download the MS COCO dataset and the directory structure should be like:

mepu-owod/
└── datasets/
    └── coco/
        ├── annotations/
        ├── train2017/
        └── val2017/

Prepare dataset for S-OWOD:

sh prepare_dataset.sh

The training dataset for S-OWODB and M-OWODB should be like:

mepu-owod/
└── datasets/
    └── mowod/
        ├── Annotations/
        ├── ImageSets/
        └── JPEGImages/
    └── sowod/
        ├── Annotations/
        ├── ImageSets/
        └── JPEGImages/

Installation

conda create -n mepu python=3.8
conda activate mepu
conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=10.2 -c pytorch

git clone https://github.com/facebookresearch/detectron2.git
cd detectron2
pip install -e . 
cd .. 
pip install -r requirements.txt

Training and Testing

Unsupervised pretraining of REW:

sh script/train_rew.sh

Train MEPU on the S-OWOD benchmark:

sh script/train_mepu_fs.sh

Evaluate MEPU on the S-OWOD benchmark:

sh script/eval_owod.sh

Results