RefineMask (CVPR 2021)

RefineMask: Towards High-Quality Instance Segmentation
with Fine-Grained Features (CVPR 2021)

This repo is the official implementation of RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features.

Framework

Main Results

Results on COCO

Note: No data augmentations except standard horizontal flipping were used.

Results on LVIS

Results on Cityscapes

Efficiency of RefineMask

Usage

Requirements

• Python 3.6+
• Pytorch 1.5.0
• mmcv-full 1.0.5

Datasets

data
  ├── coco
  |   ├── annotations
  │   │   │   ├── instances_train2017.json
  │   │   │   ├── instances_val2017.json
  │   │   │   ├── lvis_v0.5_val_cocofied.json
  │   ├── train2017
  │   │   ├── 000000004134.png
  │   │   ├── 000000031817.png
  │   │   ├── ......
  │   ├── val2017
  │   ├── test2017
  ├── lvis
  |   ├── annotations
  │   │   │   ├── lvis_v1_train.json
  │   │   │   ├── lvis_v1_val.json
  │   ├── train2017
  │   │   ├── 000000004134.png
  │   │   ├── 000000031817.png
  │   │   ├── ......
  │   ├── val2017
  │   ├── test2017
  ├── cityscapes
  |   ├── annotations
  │   │   │   ├── instancesonly_filtered_gtFine_train.json
  │   │   │   ├── instancesonly_filtered_gtFine_val.json
  │   ├── leftImg8bit
  │   |   ├── train
  │   │   ├── val
  │   │   ├── test

Note: We used the lvis-v1.0 dataset which consists of 1203 categories.

Training

./scripts/dist_train.sh ./configs/refinemask/coco/r50-refinemask-1x.py 8

Note: The codes only support batch size 1 per GPU, and we trained all models with a total batch size 16x1. If you train models with a total batch size 8x1, the performance may drop. We will support batch size 2 or more per GPU later. You can use ./scripts/slurm_train.sh for training with multi-nodes.

Inference

./scripts/dist_test.sh ./configs/refinemask/coco/r50-refinemask-1x.py xxxx.pth 8

Citation

@article{zhang2021refinemask,
  title={RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features},
  author={Gang, Zhang and Xin, Lu and Jingru, Tan and Jianmin, Li and Zhaoxiang, Zhang and Quanquan, Li and Xiaolin, Hu},
  journal={arXiv preprint arXiv:2104.08569},
  year={2021}
}