38 lines
2.2 KiB
Markdown
38 lines
2.2 KiB
Markdown
# Res2Net
|
|
|
|
## Introduction
|
|
|
|
- Res2Net: A New Multi-scale Backbone Architecture: [https://arxiv.org/abs/1904.01169](https://arxiv.org/abs/1904.01169)
|
|
|
|
```
|
|
@article{DBLP:journals/corr/abs-1904-01169,
|
|
author = {Shanghua Gao and
|
|
Ming{-}Ming Cheng and
|
|
Kai Zhao and
|
|
Xinyu Zhang and
|
|
Ming{-}Hsuan Yang and
|
|
Philip H. S. Torr},
|
|
title = {Res2Net: {A} New Multi-scale Backbone Architecture},
|
|
journal = {CoRR},
|
|
volume = {abs/1904.01169},
|
|
year = {2019},
|
|
url = {http://arxiv.org/abs/1904.01169},
|
|
archivePrefix = {arXiv},
|
|
eprint = {1904.01169},
|
|
timestamp = {Thu, 25 Apr 2019 10:24:54 +0200},
|
|
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1904-01169},
|
|
bibsource = {dblp computer science bibliography, https://dblp.org}
|
|
}
|
|
```
|
|
|
|
|
|
## Model Zoo
|
|
|
|
| Backbone | Type | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP | Download | Configs |
|
|
| :---------------------- | :------------- | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
|
|
| Res2Net50-FPN | Faster | 2 | 1x | - | 40.6 | - | [model](https://paddledet.bj.bcebos.com/models/faster_rcnn_res2net50_vb_26w_4s_fpn_1x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/res2net/faster_rcnn_res2net50_vb_26w_4s_fpn_1x_coco.yml) |
|
|
| Res2Net50-FPN | Mask | 2 | 2x | - | 42.4 | 38.1 | [model](https://paddledet.bj.bcebos.com/models/mask_rcnn_res2net50_vb_26w_4s_fpn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/res2net/mask_rcnn_res2net50_vb_26w_4s_fpn_2x_coco.yml) |
|
|
| Res2Net50-vd-FPN | Mask | 2 | 2x | - | 42.6 | 38.1 | [model](https://paddledet.bj.bcebos.com/models/mask_rcnn_res2net50_vd_26w_4s_fpn_2x_coco.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/blob/develop/configs/res2net/mask_rcnn_res2net50_vd_26w_4s_fpn_2x_coco.yml) |
|
|
|
|
Note: all the above models are trained with 8 gpus.
|