PiClick: A Python repository from cilinyan

PiClick: Picking the desired mask in click-based interactive segmentation

Visual Results

Environment

Training and evaluation environment: Python3.8.16, PyTorch 1.11.0, CentOS 7.9, CUDA 11.4, mmcv-full 1.6.2. Run the following command to install required packages.

pip3 install -r requirements.txt

You also need to configue the paths to the datasets in config.yml before training or testing.

Evaluation

Before evaluation, please download the datasets and models, and then configure the path in config.yml.

Use the following code to evaluate the base model.

# ViT-B
python scripts/evaluate_model.py NoBRS --gpu=0 \
  --checkpoint=./weights/piclick_base448.pth \
  --eval-mode=cvpr \
  --datasets=GrabCut,Berkeley,SBD,DAVIS,PascalVOC,COCO_MVal,ssTEM,BraTS,OAIZIB
# ViT-L
python scripts/evaluate_model.py NoBRS --gpu=0 \
  --checkpoint=./weights/piclick_large448.pth \
  --eval-mode=cvpr \
  --datasets=GrabCut,Berkeley,SBD,DAVIS,PascalVOC,COCO_MVal,ssTEM,BraTS,OAIZIB
# ViT-H
python scripts/evaluate_model.py NoBRS --gpu=0 \
  --checkpoint=./weights/piclick_huge448.pth \
  --eval-mode=cvpr \
  --datasets=GrabCut,Berkeley,SBD,DAVIS,PascalVOC,COCO_MVal,ssTEM,BraTS,OAIZIB

Training

Before training, please download the MAE pretrained weights (click to download: ViT-Base, ViT-Large, ViT-Huge).

Use the following code to train a base model on COCO+LVIS dataset:

# ViT-B
python -m torch.distributed.launch --nproc_per_node=8 --master_port=59566 --use_env train.py \
  models/iter_mask/piclick_base448_cocolvis_itermask.py \
  --batch-size=136 \
  --ngpus=8
# ViT-L
python -m torch.distributed.launch --nproc_per_node=8 --master_port=59516 --use_env train.py \
  models/iter_mask/piclick_large448_cocolvis_itermask.py \
  --batch-size=56 \
  --ngpus=8
# ViT-H (A100, 80G)
python -m torch.distributed.launch --nproc_per_node=8 --master_port=59516 --use_env train.py \
  models/iter_mask/piclick_huge448_cocolvis_itermask.py \
  --batch-size=96 \
  --ngpus=8 \
  --workers 48

Download

PiClick models: Google Drive, Baidu Wangpan

BraTS dataset (369 cases): Google Drive

OAI-ZIB dataset (150 cases): Google Drive

SBD dataset: Google Drive

Other datasets: RITM Github

License

The code is released under the MIT License. It is a short, permissive software license. Basically, you can do whatever you want as long as you include the original copyright and license notice in any copy of the software/source.

Note

The according annotation platform will be released upon publication.

Citation

@article{yan2023piclick,
  title={PiClick: Picking the desired mask in click-based interactive segmentation},
  author={Yan, Cilin and Wang, Haochen and Liu, Jie and Jiang, Xiaolong and Hu, Yao and Tang, Xu and Kang, Guoliang and Gavves, Efstratios},
  journal={arXiv preprint arXiv:2304.11609},
  year={2023}
}

Acknowledgement

Our project is developed based on RITM, SimpleClick and mmdetection. We thank the authors for their great work.

cilinyan/PiClick