移动paddle_detection

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### Deformable ConvNets v2
| 骨架网络 | 网络类型 | 卷积 | 每张GPU图片个数 | 学习率策略 |推理时间(fps)| Box AP | Mask AP | 下载 | 配置文件 |
| :------------------- | :------------- | :-----: |:--------: | :-----: | :-----------: |:----: | :-----: | :----------------------------------------------------------: | :----: |
| ResNet50-FPN | Faster | c3-c5 | 1 | 1x | - | 42.1 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_dcn_r50_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/faster_rcnn_dcn_r50_fpn_1x_coco.yml) |
| ResNet50-vd-FPN | Faster | c3-c5 | 1 | 1x | - | 42.7 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_dcn_r50_vd_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/faster_rcnn_dcn_r50_vd_fpn_1x_coco.yml) |
| ResNet50-vd-FPN | Faster | c3-c5 | 1 | 2x | - | 43.7 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_dcn_r50_vd_fpn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/faster_rcnn_dcn_r50_vd_fpn_2x_coco.yml) |
| ResNet101-vd-FPN | Faster | c3-c5 | 1 | 1x | - | 45.1 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_dcn_r101_vd_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/faster_rcnn_dcn_r101_vd_fpn_1x_coco.yml) |
| ResNeXt101-vd-FPN | Faster | c3-c5 | 1 | 1x | - | 46.5 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.pdparams) |[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/faster_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.yml) |
| ResNet50-FPN | Mask | c3-c5 | 1 | 1x | - | 42.7 | 38.4 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_dcn_r50_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/mask_rcnn_dcn_r50_fpn_1x_coco.yml) |
| ResNet50-vd-FPN | Mask | c3-c5 | 1 | 2x | - | 44.6 | 39.8 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_dcn_r50_vd_fpn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/mask_rcnn_dcn_r50_vd_fpn_2x_coco.yml) |
| ResNet101-vd-FPN | Mask | c3-c5 | 1 | 1x | - | 45.6 | 40.6 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_dcn_r101_vd_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/mask_rcnn_dcn_r101_vd_fpn_1x_coco.yml) |
| ResNeXt101-vd-FPN | Mask | c3-c5 | 1 | 1x | - | 47.3 | 42.0 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/mask_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.yml) |
| ResNet50-FPN | Cascade Faster | c3-c5 | 1 | 1x | - | 42.1 | - | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_rcnn_dcn_r50_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/cascade_rcnn_dcn_r50_fpn_1x_coco.yml) |
| ResNeXt101-vd-FPN | Cascade Faster | c3-c5 | 1 | 1x | - | 48.8 | - | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/cascade_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.yml) |
**注意事项:**
- Deformable卷积网络v2(dcn_v2)参考自论文[Deformable ConvNets v2](https://arxiv.org/abs/1811.11168).
- `c3-c5`意思是在resnet模块的3到5阶段增加`dcn`.
## Citations
```
@inproceedings{dai2017deformable,
title={Deformable Convolutional Networks},
author={Dai, Jifeng and Qi, Haozhi and Xiong, Yuwen and Li, Yi and Zhang, Guodong and Hu, Han and Wei, Yichen},
booktitle={Proceedings of the IEEE international conference on computer vision},
year={2017}
}
@article{zhu2018deformable,
title={Deformable ConvNets v2: More Deformable, Better Results},
author={Zhu, Xizhou and Hu, Han and Lin, Stephen and Dai, Jifeng},
journal={arXiv preprint arXiv:1811.11168},
year={2018}
}
```

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_BASE_: [
'../datasets/coco_detection.yml',
'../runtime.yml',
'../cascade_rcnn/_base_/optimizer_1x.yml',
'../cascade_rcnn/_base_/cascade_rcnn_r50_fpn.yml',
'../cascade_rcnn/_base_/cascade_fpn_reader.yml',
]
weights: output/cascade_rcnn_dcn_r50_fpn_1x_coco/model_final
ResNet:
depth: 50
norm_type: bn
freeze_at: 0
return_idx: [0,1,2,3]
num_stages: 4
dcn_v2_stages: [1,2,3]

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_BASE_: [
'cascade_rcnn_dcn_r50_fpn_1x_coco.yml',
]
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNeXt101_vd_64x4d_pretrained.pdparams
weights: output/cascade_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco/model_final
ResNet:
depth: 101
groups: 64
base_width: 4
variant: d
norm_type: bn
freeze_at: 0
return_idx: [0,1,2,3]
num_stages: 4
dcn_v2_stages: [1,2,3]

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_BASE_: [
'faster_rcnn_dcn_r50_fpn_1x_coco.yml',
]
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet101_vd_pretrained.pdparams
weights: output/faster_rcnn_dcn_r101_vd_fpn_1x_coco/model_final
ResNet:
# index 0 stands for res2
depth: 101
variant: d
norm_type: bn
freeze_at: 0
return_idx: [0,1,2,3]
num_stages: 4
dcn_v2_stages: [1,2,3]

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_BASE_: [
'../datasets/coco_detection.yml',
'../runtime.yml',
'../faster_rcnn/_base_/optimizer_1x.yml',
'../faster_rcnn/_base_/faster_rcnn_r50_fpn.yml',
'../faster_rcnn/_base_/faster_fpn_reader.yml',
]
weights: output/faster_rcnn_dcn_r50_fpn_1x_coco/model_final
ResNet:
depth: 50
norm_type: bn
freeze_at: 0
return_idx: [0,1,2,3]
num_stages: 4
dcn_v2_stages: [1,2,3]

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_BASE_: [
'faster_rcnn_dcn_r50_fpn_1x_coco.yml',
]
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_pretrained.pdparams
weights: output/faster_rcnn_dcn_r50_vd_fpn_2x_coco/model_final
ResNet:
# index 0 stands for res2
depth: 50
variant: d
norm_type: bn
freeze_at: 0
return_idx: [0,1,2,3]
num_stages: 4
dcn_v2_stages: [1,2,3]

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_BASE_: [
'faster_rcnn_dcn_r50_fpn_1x_coco.yml',
]
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_pretrained.pdparams
weights: output/faster_rcnn_dcn_r50_vd_fpn_2x_coco/model_final
ResNet:
# index 0 stands for res2
depth: 50
variant: d
norm_type: bn
freeze_at: 0
return_idx: [0,1,2,3]
num_stages: 4
dcn_v2_stages: [1,2,3]
epoch: 24
LearningRate:
base_lr: 0.01
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones: [16, 22]
- !LinearWarmup
start_factor: 0.1
steps: 1000

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_BASE_: [
'faster_rcnn_dcn_r50_fpn_1x_coco.yml',
]
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNeXt101_vd_64x4d_pretrained.pdparams
weights: output/faster_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco/model_final
ResNet:
# for ResNeXt: groups, base_width, base_channels
depth: 101
groups: 64
base_width: 4
variant: d
norm_type: bn
freeze_at: 0
return_idx: [0,1,2,3]
num_stages: 4
dcn_v2_stages: [1,2,3]

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_BASE_: [
'mask_rcnn_dcn_r50_fpn_1x_coco.yml',
]
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet101_vd_pretrained.pdparams
weights: output/mask_rcnn_dcn_r101_vd_fpn_1x_coco/model_final
ResNet:
# index 0 stands for res2
depth: 101
variant: d
norm_type: bn
freeze_at: 0
return_idx: [0,1,2,3]
num_stages: 4
dcn_v2_stages: [1,2,3]

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_BASE_: [
'../datasets/coco_instance.yml',
'../runtime.yml',
'../mask_rcnn/_base_/optimizer_1x.yml',
'../mask_rcnn/_base_/mask_rcnn_r50_fpn.yml',
'../mask_rcnn/_base_/mask_fpn_reader.yml',
]
weights: output/mask_rcnn_dcn_r50_fpn_1x_coco/model_final
ResNet:
depth: 50
norm_type: bn
freeze_at: 0
return_idx: [0,1,2,3]
num_stages: 4
dcn_v2_stages: [1,2,3]

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_BASE_: [
'mask_rcnn_dcn_r50_fpn_1x_coco.yml',
]
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_pretrained.pdparams
weights: output/mask_rcnn_dcn_r50_vd_fpn_2x_coco/model_final
ResNet:
# index 0 stands for res2
depth: 50
variant: d
norm_type: bn
freeze_at: 0
return_idx: [0,1,2,3]
num_stages: 4
dcn_v2_stages: [1,2,3]
epoch: 24
LearningRate:
base_lr: 0.01
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones: [16, 22]
- !LinearWarmup
start_factor: 0.1
steps: 1000

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_BASE_: [
'mask_rcnn_dcn_r50_fpn_1x_coco.yml',
]
pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNeXt101_vd_64x4d_pretrained.pdparams
weights: output/mask_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco/model_final
ResNet:
# for ResNeXt: groups, base_width, base_channels
depth: 101
variant: d
groups: 64
base_width: 4
norm_type: bn
freeze_at: 0
return_idx: [0,1,2,3]
num_stages: 4
dcn_v2_stages: [1,2,3]