移动paddle_detection
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### Deformable ConvNets v2
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| 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 |
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| :------------------- | :------------- | :-----: |:--------: | :-----: | :-----------: |:----: | :-----: | :----------------------------------------------------------: | :----: |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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| 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) |
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**注意事项:**
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- Deformable卷积网络v2(dcn_v2)参考自论文[Deformable ConvNets v2](https://arxiv.org/abs/1811.11168).
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- `c3-c5`意思是在resnet模块的3到5阶段增加`dcn`.
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## Citations
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```
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@inproceedings{dai2017deformable,
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title={Deformable Convolutional Networks},
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author={Dai, Jifeng and Qi, Haozhi and Xiong, Yuwen and Li, Yi and Zhang, Guodong and Hu, Han and Wei, Yichen},
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booktitle={Proceedings of the IEEE international conference on computer vision},
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year={2017}
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}
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@article{zhu2018deformable,
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title={Deformable ConvNets v2: More Deformable, Better Results},
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author={Zhu, Xizhou and Hu, Han and Lin, Stephen and Dai, Jifeng},
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journal={arXiv preprint arXiv:1811.11168},
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year={2018}
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}
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```
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_BASE_: [
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'../datasets/coco_detection.yml',
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'../runtime.yml',
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'../cascade_rcnn/_base_/optimizer_1x.yml',
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'../cascade_rcnn/_base_/cascade_rcnn_r50_fpn.yml',
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'../cascade_rcnn/_base_/cascade_fpn_reader.yml',
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]
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weights: output/cascade_rcnn_dcn_r50_fpn_1x_coco/model_final
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ResNet:
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depth: 50
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norm_type: bn
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freeze_at: 0
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return_idx: [0,1,2,3]
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num_stages: 4
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dcn_v2_stages: [1,2,3]
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_BASE_: [
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'cascade_rcnn_dcn_r50_fpn_1x_coco.yml',
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]
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pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNeXt101_vd_64x4d_pretrained.pdparams
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weights: output/cascade_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco/model_final
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ResNet:
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depth: 101
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groups: 64
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base_width: 4
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variant: d
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norm_type: bn
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freeze_at: 0
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return_idx: [0,1,2,3]
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num_stages: 4
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dcn_v2_stages: [1,2,3]
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_BASE_: [
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'faster_rcnn_dcn_r50_fpn_1x_coco.yml',
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]
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pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet101_vd_pretrained.pdparams
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weights: output/faster_rcnn_dcn_r101_vd_fpn_1x_coco/model_final
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ResNet:
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# index 0 stands for res2
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depth: 101
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variant: d
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norm_type: bn
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freeze_at: 0
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return_idx: [0,1,2,3]
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num_stages: 4
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dcn_v2_stages: [1,2,3]
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_BASE_: [
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'../datasets/coco_detection.yml',
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'../runtime.yml',
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'../faster_rcnn/_base_/optimizer_1x.yml',
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'../faster_rcnn/_base_/faster_rcnn_r50_fpn.yml',
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'../faster_rcnn/_base_/faster_fpn_reader.yml',
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]
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weights: output/faster_rcnn_dcn_r50_fpn_1x_coco/model_final
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ResNet:
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depth: 50
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norm_type: bn
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freeze_at: 0
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return_idx: [0,1,2,3]
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num_stages: 4
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dcn_v2_stages: [1,2,3]
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_BASE_: [
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'faster_rcnn_dcn_r50_fpn_1x_coco.yml',
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]
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pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_pretrained.pdparams
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weights: output/faster_rcnn_dcn_r50_vd_fpn_2x_coco/model_final
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ResNet:
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# index 0 stands for res2
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depth: 50
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variant: d
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norm_type: bn
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freeze_at: 0
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return_idx: [0,1,2,3]
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num_stages: 4
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dcn_v2_stages: [1,2,3]
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_BASE_: [
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'faster_rcnn_dcn_r50_fpn_1x_coco.yml',
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]
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pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_pretrained.pdparams
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weights: output/faster_rcnn_dcn_r50_vd_fpn_2x_coco/model_final
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ResNet:
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# index 0 stands for res2
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depth: 50
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variant: d
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norm_type: bn
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freeze_at: 0
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return_idx: [0,1,2,3]
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num_stages: 4
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dcn_v2_stages: [1,2,3]
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epoch: 24
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LearningRate:
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base_lr: 0.01
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schedulers:
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- !PiecewiseDecay
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gamma: 0.1
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milestones: [16, 22]
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- !LinearWarmup
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start_factor: 0.1
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steps: 1000
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_BASE_: [
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'faster_rcnn_dcn_r50_fpn_1x_coco.yml',
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]
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pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNeXt101_vd_64x4d_pretrained.pdparams
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weights: output/faster_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco/model_final
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ResNet:
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# for ResNeXt: groups, base_width, base_channels
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depth: 101
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groups: 64
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base_width: 4
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variant: d
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norm_type: bn
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freeze_at: 0
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return_idx: [0,1,2,3]
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num_stages: 4
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dcn_v2_stages: [1,2,3]
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_BASE_: [
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'mask_rcnn_dcn_r50_fpn_1x_coco.yml',
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]
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pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet101_vd_pretrained.pdparams
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weights: output/mask_rcnn_dcn_r101_vd_fpn_1x_coco/model_final
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ResNet:
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# index 0 stands for res2
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depth: 101
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variant: d
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norm_type: bn
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freeze_at: 0
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return_idx: [0,1,2,3]
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num_stages: 4
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dcn_v2_stages: [1,2,3]
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_BASE_: [
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'../datasets/coco_instance.yml',
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'../runtime.yml',
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'../mask_rcnn/_base_/optimizer_1x.yml',
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'../mask_rcnn/_base_/mask_rcnn_r50_fpn.yml',
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'../mask_rcnn/_base_/mask_fpn_reader.yml',
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]
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weights: output/mask_rcnn_dcn_r50_fpn_1x_coco/model_final
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ResNet:
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depth: 50
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norm_type: bn
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freeze_at: 0
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return_idx: [0,1,2,3]
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num_stages: 4
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dcn_v2_stages: [1,2,3]
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_BASE_: [
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'mask_rcnn_dcn_r50_fpn_1x_coco.yml',
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]
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pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_pretrained.pdparams
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weights: output/mask_rcnn_dcn_r50_vd_fpn_2x_coco/model_final
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ResNet:
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# index 0 stands for res2
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depth: 50
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variant: d
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norm_type: bn
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freeze_at: 0
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return_idx: [0,1,2,3]
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num_stages: 4
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dcn_v2_stages: [1,2,3]
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epoch: 24
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LearningRate:
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base_lr: 0.01
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schedulers:
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- !PiecewiseDecay
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gamma: 0.1
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milestones: [16, 22]
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- !LinearWarmup
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start_factor: 0.1
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steps: 1000
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@@ -0,0 +1,17 @@
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_BASE_: [
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'mask_rcnn_dcn_r50_fpn_1x_coco.yml',
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]
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pretrain_weights: https://paddledet.bj.bcebos.com/models/pretrained/ResNeXt101_vd_64x4d_pretrained.pdparams
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weights: output/mask_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco/model_final
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ResNet:
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# for ResNeXt: groups, base_width, base_channels
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depth: 101
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variant: d
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groups: 64
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base_width: 4
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norm_type: bn
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freeze_at: 0
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return_idx: [0,1,2,3]
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num_stages: 4
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dcn_v2_stages: [1,2,3]
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