Official PyTorch implementation for EOPSN .
We propose open-set panoptic segmentation task and propose a new baseline called EOPSN. The code is based on Detectron2
First, install requirements.
pip install -r requirements.txt
Then, install PyTorch 1.5+ and torchvision 0.6+:
conda install -c pytorch pytorch torchvision
Finally, you need to install Detectron2.
To prevent version conflict, I recommand to install via included detectron2 folders.
Regarding installation issue caused from detectron2, please refer to here.
cd detectron2
pip install -e ./
Download and extract COCO 2017 train and val images with annotations from http://cocodataset.org. We expect the directory structure to be the following:
datasets/coco
annotations/ # annotation json files
train2017/ # train images
val2017/ # val images
To convert closed-set panoptic segmentation to open-set panoptic segmentation, run:
python prepare_unknown.py
The default setting is K=20, you can change here.
To train EOPSN on a single node with 8 gpus for 30,000 iterations run:
python train_net.py --config configs/EOPSN_K20.yaml --num-gpus 8
Note that it requires pre-trained models (Void-suppression). Please download from Goolge Drive.
To train baseline (train) on a single node with 8 gpus for 45,000 iterations run:
python train_net.py --config configs/baseline_K20.yaml --num-gpus 8
If you want to log using WandB, you can add --wandb flag.
To evaluate EOPSN on COCO val5k with a single GPU run:
python train_net.py --config configs/EOPSN_K20.yaml --num-gpus 8 --resume --eval-only
@inproceedings{hwang2021exemplar,
author = {Hwang, Jaedong and Oh, Seoung Wug and Lee, Joon-Young and Han, Bohyung},
title = {Exemplar-Based Open-Set Panoptic Segmentation Network},
booktitle = {CVPR},
year = {2021},
}
EOPSN is released under the CC BY-NC-SA 4.0 license. Please see the LICENSE file for more information. The detectron2 part is released under the Apache 2.0 license. Please see the detectron2/LICENSE file for more information.
We actively welcome your pull requests!