# Res2Net for object detection and instance segmentation

## Introduction

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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.

|    Backbone     |Params. | GFLOPs  | top-1 err. | top-5 err. |
| :-------------: |:----:  | :-----: | :--------: | :--------: |
| ResNet-101      |44.6 M  | 7.8     |  22.63     |  6.44      |
| ResNeXt-101-64x4d |83.5M | 15.5    |  20.40     |  -         |
| HRNetV2p-W48    | 77.5M  | 16.1    |  20.70     |  5.50      |
| Res2Net-101     | 45.2M  | 8.3     |  18.77     |  4.64      |

Compared with other backbone networks, Res2Net requires fewer parameters and FLOPs.

**Note:**

- GFLOPs for classification are calculated with image size (224x224).

```latex
@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},
}
```

## Results and Models

### Faster R-CNN

|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: |
|R2-101-FPN       | pytorch |   2x   |   7.4    |   -           |  43.0  |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco_20200514_231734.log.json) |

### Mask R-CNN

|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: |
|R2-101-FPN       | pytorch |    2x   |   7.9    |      -         |   43.6 | 38.7  |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/mask_rcnn_r2_101_fpn_2x_coco/mask_rcnn_r2_101_fpn_2x_coco-17f061e8.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/mask_rcnn_r2_101_fpn_2x_coco/mask_rcnn_r2_101_fpn_2x_coco_20200515_002413.log.json) |

### Cascade R-CNN

|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: |
|R2-101-FPN       | pytorch |   20e   |   7.8    |      -         |  45.7  |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_rcnn_r2_101_fpn_20e_coco/cascade_rcnn_r2_101_fpn_20e_coco-f4b7b7db.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_rcnn_r2_101_fpn_20e_coco/cascade_rcnn_r2_101_fpn_20e_coco_20200515_091644.log.json) |

### Cascade Mask R-CNN

|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: |
R2-101-FPN       | pytorch |  20e   |    9.5  |      -         |  46.4  |  40.0  |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco-8a7b41e1.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco_20200515_091645.log.json) |

### Hybrid Task Cascade (HTC)

|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download |
| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: |
| R2-101-FPN     | pytorch |   20e   |    -    |      -         |  47.5  | 41.6  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/htc_r2_101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/htc_r2_101_fpn_20e_coco/htc_r2_101_fpn_20e_coco-3a8d2112.pth) &#124; [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/htc_r2_101_fpn_20e_coco/htc_r2_101_fpn_20e_coco_20200515_150029.log.json) |

- Res2Net ImageNet pretrained models are in [Res2Net-PretrainedModels](https://github.com/Res2Net/Res2Net-PretrainedModels).
- More applications of Res2Net are in [Res2Net-Github](https://github.com/Res2Net/).
