OpenSeeD
This is the official implementation of the paper "A Simple Framework for Open-Vocabulary Segmentation and Detection".
You can also find the more detailed demo at video link on Youtube.
:point_right: [New] demo code is available :point_right: [New] OpenSeeD has been accepted to ICCV 2023! training code is available!
:rocket: Key Features
- A Simple Framework for Open-Vocabulary Segmentation and Detection.
- Support interactive segmentation with box input to generate mask.
:bulb: Installation
pip3 install torch==1.13.1 torchvision==0.14.1 --extra-index-url https://download.pytorch.org/whl/cu113
python -m pip install 'git+https://github.com/MaureenZOU/detectron2-xyz.git'
pip install git+https://github.com/cocodataset/panopticapi.git
python -m pip install -r requirements.txt
export DATASET=/pth/to/dataset
Download the pretrained checkpoint from here.
:bulb: Demo script
python demo/demo_panoseg.py evaluate --conf_files configs/openseed/openseed_swint_lang.yaml --image_path images/animals.png --overrides WEIGHT /path/to/ckpt/model_state_dict_swint_51.2ap.pt
:fire: Remember to modify the vocabulary thing_classes and stuff_classes in demo_panoseg.py if your want to segment open-vocabulary objects.
Evaluation on coco
python train_net.py --original_load --eval_only --num-gpus 8 --config-file configs/openseed/openseed_swint_lang.yaml MODEL.WEIGHTS=[/path/to/lang/weight](https://github.com/IDEA-Research/OpenSeeD/releases/download/openseed/model_state_dict_swint_51.2ap.pt)
You are expected to get 55.4 PQ.
:bulb: Some coco-format data
Here is the coco-format json file for evaluating BDD and SUN.
Training OpenSeeD baseline
Training on coco
python train_net.py --num-gpus 8 --config-file configs/openseed/openseed_swint_lang.yaml --lang_weight [/path/to/lang/weight](https://github.com/IDEA-Research/OpenSeeD/releases/download/training/model_state_dict_only_language.pt)
Training on coco+o365
python train_net.py --num-gpus 8 --config-file configs/openseed/openseed_swint_lang_o365.yaml --lang_weight [/path/to/lang/weight](https://github.com/IDEA-Research/OpenSeeD/releases/download/training/model_state_dict_only_language.pt)
Checkpoints
- Swin-T model trained on COCO panoptic segmentation and Objects365 weights.
- Swin-L model fine-tuned on COCO panoptic segmentation weights.
- Swin-L model fine-tuned on ADE20K semantic segmentation weights.
:unicorn: Model Framework
:volcano: Results
Results on open segmentation
Results on task transfer and segmentation in the wild
Citing OpenSeeD
If you find our work helpful for your research, please consider citing the following BibTeX entry.
@article{zhang2023simple,
title={A Simple Framework for Open-Vocabulary Segmentation and Detection},
author={Zhang, Hao and Li, Feng and Zou, Xueyan and Liu, Shilong and Li, Chunyuan and Gao, Jianfeng and Yang, Jianwei and Zhang, Lei},
journal={arXiv preprint arXiv:2303.08131},
year={2023}
}