OpenMMLab Detection Toolbox and Benchmark https://mmdetection.readthedocs.io/
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README.md
metafile.yml
queryinst_r50_fpn_1x_coco.py
queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
queryinst_r50_fpn_mstrain_480-800_3x_coco.py
queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
queryinst_r101_fpn_mstrain_480-800_3x_coco.py

README.md

Instances as Queries

Introduction

@InProceedings{Fang_2021_ICCV,
    author    = {Fang, Yuxin and Yang, Shusheng and Wang, Xinggang and Li, Yu and Fang, Chen and Shan, Ying and Feng, Bin and Liu, Wenyu},
    title     = {Instances As Queries},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2021},
    pages     = {6910-6919}
}

Results and Models

Model Backbone Style Lr schd Number of Proposals Multi-Scale RandomCrop box AP mask AP Config Download
QueryInst R-50-FPN pytorch 1x 100 False False 42.0 37.5 config model | log
QueryInst R-50-FPN pytorch 3x 100 True False 44.8 39.8 config model | log
QueryInst R-50-FPN pytorch 3x 300 True True 47.5 41.7 config model | log
QueryInst R-101-FPN pytorch 3x 100 True False 46.4 41.0 config model | log
QueryInst R-101-FPN pytorch 3x 300 True True 49.0 42.9 config model | log