Awesome
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
<a name="CitingOpenSeeD"></a>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}
}