We propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer.
Name | lr sched |
train mem (GB) |
box AP |
mask AP |
PQ | download |
---|---|---|---|---|---|---|
R50-FPN | 1x | 4.8 | 37.6 | 34.7 | 39.4 | model | metrics |
R50-FPN | 3x | 4.8 | 40.0 | 36.5 | 41.5 | model | metrics |
R101-FPN | 3x | 6.0 | 42.4 | 38.5 | 43.0 | model | metrics |
Res2Net101-FPN | 3x | 6.0 | 44.0 | 39.6 | 44.5 | model | metrics |
./tools/train_net.py --num-gpus 8 --config-file configs/COCO-PanopticSegmentation/panoptic_fpn_R2_101_3x.yaml
If you find this work or code is helpful in your research, please cite:
@article{gao2019res2net,
title={Res2Net: A New Multi-scale Backbone Architecture},
author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip},
journal={IEEE TPAMI},
year={2020},
doi={10.1109/TPAMI.2019.2938758},
}
For more details of detectron2, please refer to the detectron2 repo.
Detectron2 is Facebook AI Research's next generation software system that implements state-of-the-art object detection algorithms. It is a ground-up rewrite of the previous version, Detectron, and it originates from maskrcnn-benchmark.
See our blog post to see more demos and learn about detectron2.
See INSTALL.md.
See GETTING_STARTED.md, or the Colab Notebook.
Learn more at our documentation. And see projects/ for some projects that are built on top of detectron2.
We provide a large set of baseline results and trained models available for download in the Detectron2 Model Zoo.
Detectron2 is released under the Apache 2.0 license.
If you use Detectron2 in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry.
@misc{wu2019detectron2,
author = {Yuxin Wu and Alexander Kirillov and Francisco Massa and
Wan-Yen Lo and Ross Girshick},
title = {Detectron2},
howpublished = {\url{https://github.com/facebookresearch/detectron2}},
year = {2019}
}