Mask Transfiner

Mask Transfiner for High-Quality Instance Segmentation [Mask Transfiner, CVPR 2022].

This is the official pytorch implementation of Transfiner built on the open-source detectron2. Our project website contains more information, including the visual slider comparison: vis.xyz/pub/transfiner.

Mask Transfiner for High-Quality Instance Segmentation
Lei Ke, Martin Danelljan, Xia Li, Yu-Wing Tai, Chi-Keung Tang, Fisher Yu
CVPR, 2022

Updates

:fire::fire: We released the Video Mask Transfiner and HQ-YTVIS benchmark in ECCV'2022.

Highlights

Transfiner: High-quality instance segmentation with state-of-the-art performance and extreme details.
Novelty: An efficient transformer targeting for high-resolution instance masks predictions based on the quadtree structure.
Efficacy: Large mask and boundary AP improvements on three instance segmentation benchmarks, including COCO, Cityscapes and BDD100k.
Simple: Small additional computation burden compared to standard transformer and easy to use.

:fire::fire: Play with our Mask Transfiner demo at , supported by Huggingface Spaces.

Mask Transfiner with Quadtree Transformer

Results on COCO test-dev

(Check Table 9 of the paper for full results, all methods are trained on COCO train2017. This is a reimplementation. Thus, the numbers might be slightly different from the ones reported in our original paper.)

Backbone(configs)	Method	mAP(mask)
R50-FPN	Mask R-CNN (ICCV'17)	34.2
R50-FPN	PANet (CVPR'18)	36.6
R50-FPN	MS R-CNN (CVPR'19)	35.6
R50-FPN	PointRend (1x, CVPR'20)	36.3
R50-FPN	Transfiner (1x, CVPR'22)	37.0, Pretrained Model
Res-R50-FPN	BCNet (CVPR'21)	38.4
R50-FPN	Transfiner (3x, CVPR'22)	39.2, Pretrained Model
R50-FPN-DCN	Transfiner (3x, CVPR'22)	40.5, Pretrained Model

Backbone(configs)	Method	mAP(mask)
R101-FPN	Mask R-CNN (ICCV'17)	36.1
R101-FPN	MS R-CNN (CVPR'19)	38.3
R101-FPN	BMask R-CNN (ECCV'20)	37.7
R101-FPN	SOLOv2 (NeurIPS'20)	39.7
R101-FPN	BCNet (CVPR'21)	39.8
R101-FPN	Transfiner (3x, CVPR'22)	40.5, Pretrained Model
R101-FPN-DCN	Transfiner (3x, CVPR'22)	42.2, Pretrained Model

Backbone(configs)	Pretrain	Lr Schd	Size	Method	mAP(box) on Val2017	mAP(mask) on Val2017
Swin-T,init_weight of imagenet (d2 format)	ImageNet-1k	3X	[480-800]	Transfiner	46.9	43.5, Pretrained Model
Swin-B,init_weight of imagenet (d2 format)	ImageNet-22k	3X	[480-800]	Transfiner	49.8	45.5,Pretrained Model

Results on LVIS Dataset, v0.5

Backbone(configs)	Lr Schd	Method	mAP(mask)
X101-FPN	1x	Mask R-CNN	27.1
X101-FPN	1x	Transfiner	29.2, Pretrained Model

Introduction

Two-stage and query-based instance segmentation methods have achieved remarkable results. However, their segmented masks are still very coarse. In this paper, we present Mask Transfiner for high-quality and efficient instance segmentation. Instead of operating on regular dense tensors, our Mask Transfiner decomposes and represents the image regions as a quadtree. Our transformer-based approach only processes detected error-prone tree nodes and self-corrects their errors in parallel. While these sparse pixels only constitute a small proportion of the total number, they are critical to the final mask quality. This allows Mask Transfiner to predict highly accurate instance masks, at a low computational cost. Extensive experiments demonstrate that Mask Transfiner outperforms current instance segmentation methods on three popular benchmarks, significantly improving both two-stage and query-based frameworks by a large margin of +3.0 mask AP on COCO and BDD100K, and +6.6 boundary AP on Cityscapes.

Step-by-step Installation

conda create -n transfiner python=3.7 -y
conda activate transfiner

conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=11.0 -c pytorch

# Coco api and visualization dependencies
pip install ninja yacs cython matplotlib tqdm
pip install opencv-python==4.4.0.40
# Boundary dependency
pip install scikit-image
pip install kornia==0.5.11

export INSTALL_DIR=$PWD

# install pycocotools. Please make sure you have installed cython.
cd $INSTALL_DIR
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
python setup.py build_ext install

# install transfiner
cd $INSTALL_DIR
git clone --recursive https://github.com/SysCV/transfiner.git
cd transfiner/
python3 setup.py build develop

unset INSTALL_DIR

Dataset Preparation

Prepare for coco2017 dataset and Cityscapes following this instruction.

  mkdir -p datasets/coco
  ln -s /path_to_coco_dataset/annotations datasets/coco/annotations
  ln -s /path_to_coco_dataset/train2017 datasets/coco/train2017
  ln -s /path_to_coco_dataset/test2017 datasets/coco/test2017
  ln -s /path_to_coco_dataset/val2017 datasets/coco/val2017

Multi-GPU Training and Evaluation on Validation set

Refer to our scripts folder for more traning, testing and visualization commands:

bash scripts/train_transfiner_3x_101.sh

bash scripts/train_transfiner_1x_50.sh

Pretrained Models

Download the pretrained models from the above result table:

  mkdir pretrained_model
  #And put the downloaded pretrained models in this directory.

Testing on Test-dev

bash scripts/test_3x_transfiner_101.sh

Visualization

bash scripts/visual.sh

for swin-based model:

bash scripts/visual_swinb.sh

Citation

If you find Mask Transfiner useful in your research or refer to the provided baseline results, please star :star: this repository and consider citing :pencil::

@inproceedings{transfiner,
    author={Ke, Lei and Danelljan, Martin and Li, Xia and Tai, Yu-Wing and Tang, Chi-Keung and Yu, Fisher},
    title={Mask Transfiner for High-Quality Instance Segmentation},
    booktitle = {CVPR},
    year = {2022}
}

If you are interested in Video Mask Transfiner and High-Quality Video Instance Segmentation data:

@inproceedings{vmt,
    title = {Video Mask Transfiner for High-Quality Video Instance Segmentation},
    author = {Ke, Lei and Ding, Henghui and Danelljan, Martin and Tai, Yu-Wing and Tang, Chi-Keung and Yu, Fisher},
    booktitle = {European Conference on Computer Vision (ECCV)},
    year = {2022}
}

SysCV / transfiner

readme