Skip to content

README.md

Latest commit

 

History

History
58 lines (48 loc) · 3.25 KB

README.md

File metadata and controls

58 lines (48 loc) · 3.25 KB

This is the project page for paper: Universal Image Segmentation with Efficiency.

Key Differences

This paper expands the YOSO framework for efficient unified image segmentation, encompassing panoptic, semantic, instance, and video segmentation capabilities.

The key distinctions are:

  • The "UISE/re-parameterize.ipynb" code illustrates the process of reparameterization between IFA and CFA.
  • The "UISE/uise_video/" directory demonstrates the integration of UISE into video segmentation workflows.
  • The "UISE/config/" directory shows the segmentation tasks that UISE newly supports.

Model Zoo

Click to download the corresponding model from the model zoo.

Dataset Pan Ins Sem Video
coco 48.8 PQ 32.9 mAP 58.7 mIoU -
cityscapes 59.9 PQ 32.4 mAP 79.3 mIoU -
ade20k 38.3 PQ 22.3 mAP 44.1 mIoU -
mapillary 34.2 PQ - 50.1 mIoU -
ytb19 - - - 40.8 mAP
ytb21 - - - 36.2 mAP

Getting Started

Installation

We recommend to use Anaconda for installation.

conda create -n UISE python=3.8 -y
conda activate UISE
conda install pytorch==1.10.1 torchvision==0.11.2 cudatoolkit=11.3 -c pytorch
pip install pycocotools
pip install git+https://github.com/cocodataset/panopticapi.git
pip install fvcore
pip install Pillow==8.4.0
pip install cloudpickle 
pip install timm
pip install scipy
pip install opencv-python
git clone https://github.com/hujiecpp/UISE.git
cd UISE
python setup.py develop

Datasets Preparation

See Preparing Datasets for Mask2Former.

Training & Evaluation

  • Train UISE (e.g., on COCO dataset with R50 backbone).
python UISE/train_net.py --num-gpus 4 --config-file UISE/configs/coco/panoptic-segmentation/UISE-R50.yaml
  • Evaluate UISE (e.g., on COCO dataset with R50 backbone).
python UISE/train_net.py --num-gpus 4 --config-file UISE/configs/coco/panoptic-segmentation/UISE-R50.yaml --eval-only MODEL.WEIGHTS ./model_zoo/coco_pan_seg.pth