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| # Deformable Convolutional Networks | ||
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| # Introduction | ||
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| ``` | ||
| @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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| ## Results and Models | ||
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| | Backbone | Model | Style | Conv | Pool | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download | | ||
| |:---------:|:------------:|:-------:|:-------------:|:------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:| | ||
| | R-50-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 3.9 | 0.594 | 10.2 | 40.0 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/faster_rcnn_dconv_c3-c5_r50_fpn_1x_20190125-e41688c9.pth) | | ||
| | R-50-FPN | Faster | pytorch | mdconv(c3-c5) | - | 1x | 3.7 | 0.598 | 10.0 | 40.3 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/faster_rcnn_mdconv_c3-c5_r50_fpn_1x_20190125-1b768045.pth) | | ||
| | R-50-FPN | Faster | pytorch | - | dpool | 1x | 4.6 | 0.714 | 8.7 | 37.9 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/faster_rcnn_dpool_r50_fpn_1x_20190125-f4fc1d70.pth) | | ||
| | R-50-FPN | Faster | pytorch | - | mdpool | 1x | 5.2 | 0.769 | 8.2 | 38.1 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/faster_rcnn_mdpool_r50_fpn_1x_20190125-473d0f3d.pth) | | ||
| | R-101-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 5.8 | 0.811 | 8.0 | 42.1 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/faster_rcnn_dconv_c3-c5_r101_fpn_1x_20190125-a7e31b65.pth) | | ||
| | X-101-32x4d-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 7.1 | 1.126 | 6.6 | 43.5 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/faster_rcnn_dconv_c3-c5_x101_32x4d_fpn_1x_20190201-6d46376f.pth) | | ||
| | R-50-FPN | Mask | pytorch | dconv(c3-c5) | - | 1x | 4.5 | 0.712 | 7.7 | 41.1 | 37.2 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/mask_rcnn_dconv_c3-c5_r50_fpn_1x_20190125-4f94ff79.pth) | | ||
| | R-50-FPN | Mask | pytorch | mdconv(c3-c5) | - | 1x | 4.5 | 0.712 | 7.7 | 41.4 | 37.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/mask_rcnn_mdconv_c3-c5_r50_fpn_1x_20190125-c5601dc3.pth) | | ||
| | R-101-FPN | Mask | pytorch | dconv(c3-c5) | - | 1x | 6.4 | 0.939 | 6.5 | 43.2 | 38.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/mask_rcnn_dconv_c3-c5_r101_fpn_1x_20190125-decb6db5.pth) | | ||
| | R-50-FPN | Cascade | pytorch | dconv(c3-c5) | - | 1x | 4.4 | 0.660 | 7.6 | 44.1 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/cascade_rcnn_dconv_c3-c5_r50_fpn_1x_20190125-dfa53166.pth) | | ||
| | R-101-FPN | Cascade | pytorch | dconv(c3-c5) | - | 1x | 6.3 | 0.881 | 6.8 | 45.1 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/cascade_rcnn_dconv_c3-c5_r101_fpn_1x_20190125-aaa877cc.pth) | | ||
| | R-50-FPN | Cascade Mask | pytorch | dconv(c3-c5) | - | 1x | 6.6 | 0.942 | 5.7 | 44.5 | 38.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/cascade_mask_rcnn_dconv_c3-c5_r50_fpn_1x_20190125-09d8a443.pth) | | ||
| | R-101-FPN | Cascade Mask | pytorch | dconv(c3-c5) | - | 1x | 8.5 | 1.156 | 5.1 | 45.8 | 39.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/dcn/cascade_mask_rcnn_dconv_c3-c5_r101_fpn_1x_20190125-0d62c190.pth) | | ||
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| **Notes:** | ||
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| - `dconv` and `mdconv` denote (modulated) deformable convolution, `c3-c5` means adding dconv in resnet stage 3 to 5. `dpool` and `mdpool` denote (modulated) deformable roi pooling. | ||
| - The dcn ops are modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch, which should be more memory efficient and slightly faster. |
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| # Group Normalization | ||
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| ## Introduction | ||
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| ``` | ||
| @inproceedings{wu2018group, | ||
| title={Group Normalization}, | ||
| author={Wu, Yuxin and He, Kaiming}, | ||
| booktitle={Proceedings of the European Conference on Computer Vision (ECCV)}, | ||
| year={2018} | ||
| } | ||
| ``` | ||
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| ## Results and Models | ||
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| | Backbone | model | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download | | ||
| |:-------------:|:----------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:| | ||
| | R-50-FPN (d) | Mask R-CNN | 2x | 7.2 | 0.806 | 5.4 | 39.9 | 36.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_gn_2x_20180113-86832cf2.pth) | | ||
| | R-50-FPN (d) | Mask R-CNN | 3x | 7.2 | 0.806 | 5.4 | 40.2 | 36.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_gn_3x_20180113-8e82f48d.pth) | | ||
| | R-101-FPN (d) | Mask R-CNN | 2x | 9.9 | 0.970 | 4.8 | 41.6 | 37.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r101_fpn_gn_2x_20180113-9598649c.pth) | | ||
| | R-101-FPN (d) | Mask R-CNN | 3x | 9.9 | 0.970 | 4.8 | 41.7 | 37.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r101_fpn_gn_3x_20180113-a14ffb96.pth) | | ||
| | R-50-FPN (c) | Mask R-CNN | 2x | 7.2 | 0.806 | 5.4 | 39.7 | 35.9 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_gn_contrib_2x_20180113-ec93305c.pth) | | ||
| | R-50-FPN (c) | Mask R-CNN | 3x | 7.2 | 0.806 | 5.4 | 40.1 | 36.2 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_gn_contrib_3x_20180113-9d230cab.pth) | | ||
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| **Notes:** | ||
| - (d) means pretrained model converted from Detectron, and (c) means the contributed model pretrained by [@thangvubk](https://github.com/thangvubk). | ||
| - The `3x` schedule is epoch [28, 34, 36]. |
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