@inproceedings{gupta2019lvis,
title={{LVIS}: A Dataset for Large Vocabulary Instance Segmentation},
author={Gupta, Agrim and Dollar, Piotr and Girshick, Ross},
booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition},
year={2019}
}
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Please follow install guide to install open-mmlab forked cocoapi first.
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Run following scripts to install our forked lvis-api.
pip install git+https://github.com/lvis-dataset/lvis-api.git
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All experiments use oversample strategy here with oversample threshold
1e-3
. -
The size of LVIS v0.5 is half of COCO, so schedule
2x
in LVIS is roughly the same iterations as1x
in COCO.
Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
---|---|---|---|---|---|---|---|---|
R-50-FPN | pytorch | 2x | - | - | 26.1 | 25.9 | config | model | log |
R-101-FPN | pytorch | 2x | - | - | 27.1 | 27.0 | config | model | log |
X-101-32x4d-FPN | pytorch | 2x | - | - | 26.7 | 26.9 | config | model | log |
X-101-64x4d-FPN | pytorch | 2x | - | - | 26.4 | 26.0 | config | model | log |
Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
---|---|---|---|---|---|---|---|---|
R-50-FPN | pytorch | 1x | 9.1 | - | 22.5 | 21.7 | config | model | log |
R-101-FPN | pytorch | 1x | 10.8 | - | 24.6 | 23.6 | config | model | log |
X-101-32x4d-FPN | pytorch | 1x | 11.8 | - | 26.7 | 25.5 | config | model | log |
X-101-64x4d-FPN | pytorch | 1x | 14.6 | - | 27.2 | 25.8 | config | model | log |