Crossover Learning for Fast Online Video Instance Segmentation (ICCV 2021)
by Shusheng Yang*, Yuxin Fang*, Xinggang Wang†, Yu Li, Chen Fang, Ying Shan, Bin Feng, Wenyu Liu.
(*) equal contribution, (†) corresponding author.
Name | AP | AP@50 | AP@75 | AR@1 | AR@10 | download |
---|---|---|---|---|---|---|
CrossVIS_R_50_1x | 35.5 | 55.1 | 39.0 | 35.4 | 42.2 | baidu(keycode: a0j0 ) | google |
CrossVIS_R_101_1x | 36.9 | 57.8 | 41.4 | 36.2 | 43.9 | baidu(keycode: iwwo ) | google |
First, clone the repository locally:
git clone https://github.com/hustvl/CrossVIS.git
Then, create python virtual environment with conda:
conda create --name crossvis python=3.7.2
conda activate crossvis
Install torch 1.7.0 and torchvision 0.8.1:
pip install torch==1.7.0 torchvision==0.8.1
Follow the instructions to install detectron2
. Please install detectron2
with commit id 9eb4831 if you have any issues related to detectron2
.
Then install AdelaiDet
by:
cd CrossVIS
python setup.py develop
YouTube-VIS 2019
dataset from here, the overall directory hierarchical structure is:
CrossVIS
├── datasets
│ ├── youtubevis
│ │ ├── train
│ │ │ ├── 003234408d
│ │ │ ├── ...
│ │ ├── val
│ │ │ ├── ...
│ │ ├── annotations
│ │ │ ├── train.json
│ │ │ ├── valid.json
CondInst
1x pretrained model from hereTrain CrossVIS R-50 with single GPU:
python tools/train_net.py --config configs/CrossVIS/R_50_1x.yaml MODEL.WEIGHTS $PATH_TO_CondInst_MS_R_50_1x
Train CrossVIS R-50 with multi GPUs:
python tools/train_net.py --config configs/CrossVIS/R_50_1x.yaml --num-gpus $NUM_GPUS MODEL.WEIGHTS $PATH_TO_CondInst_MS_R_50_1x
python tools/test_vis.py --config-file configs/CrossVIS/R_50_1x.yaml --json-file datasets/youtubevis/annotations/valid.json --opts MODEL.WEIGHTS $PATH_TO_CHECKPOINT
The final results will be stored in results.json
, just compress it with zip
and upload to the codalab server to get the performance on validation set.
This code is mainly based on detectron2
and AdelaiDet
, thanks for their awesome work and great contributions to the computer vision community!
If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil: :
@InProceedings{Yang_2021_ICCV,
author = {Yang, Shusheng and Fang, Yuxin and Wang, Xinggang and Li, Yu and Fang, Chen and Shan, Ying and Feng, Bin and Liu, Wenyu},
title = {Crossover Learning for Fast Online Video Instance Segmentation},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2021},
pages = {8043-8052}
}