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reorg the model zoo

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MODEL_ZOO.md
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...@@ -148,7 +148,17 @@ More models with different backbones will be added to the model zoo. ...@@ -148,7 +148,17 @@ More models with different backbones will be added to the model zoo.
### Hybrid Task Cascade (HTC) ### Hybrid Task Cascade (HTC)
Please refer to [HTC](configs/htc/README.md) for details. | Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download |
|:---------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:|
| R-50-FPN | pytorch | 1x | 7.4 | 0.936 | 3.5 | 42.2 | 37.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/htc/htc_r50_fpn_1x_20190408-878c1712.pth) |
| R-50-FPN | pytorch | 20e | - | - | - | 43.2 | 38.0 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/htc/htc_r50_fpn_20e_20190408-c03b7015.pth) |
| R-101-FPN | pytorch | 20e | 9.3 | 1.051 | 3.4 | 44.9 | 39.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/htc/htc_r101_fpn_20e_20190408-a2e586db.pth) |
| X-101-32x4d-FPN | pytorch |20e| 5.8 | 0.769 | 3.3 | 46.1 | 40.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/htc/htc_x101_32x4d_fpn_20e_20190408-9eae4d0b.pth) |
| X-101-64x4d-FPN | pytorch |20e| 7.5 | 1.120 | 3.0 | 47.0 | 40.9 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/htc/htc_x101_64x4d_fpn_20e_20190408-497f2561.pth) |
**Notes:**
- Please refer to [Hybrid Task Cascade](configs/gn/README.md) for details and more a powerful model (50.7/43.9).
### SSD ### SSD
...@@ -172,41 +182,16 @@ Please refer to [HTC](configs/htc/README.md) for details. ...@@ -172,41 +182,16 @@ Please refer to [HTC](configs/htc/README.md) for details.
### Group Normalization (GN) ### Group Normalization (GN)
| Backbone | model | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download | Please refer to [Group Normalization](configs/gn/README.md) for details.
|:-------------:|:----------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:|
| 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) |
**Notes:** ### Weight Standardization
- (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].
### Deformable Convolution v2 Please refer to [Weight Standardization](configs/gn+ws/README.md) for details.
| Backbone | Model | Style | Conv | Pool | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download | ### Deformable Convolution v2
|:---------:|:------------:|:-------:|:-------------:|:------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:|
| 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) |
**Notes:** Please refer to [Deformable Convolutional Networks](configs/dcn/README.md) for details.
- `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.
## Comparison with Detectron and maskrcnn-benchmark ## Comparison with Detectron and maskrcnn-benchmark
......
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# Deformable Convolutional Networks
# Introduction
```
@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}
}
```
## Results and Models
| 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) |
**Notes:**
- `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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configs/gn/README.md 0 → 100644
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# Group Normalization
## Introduction
```
@inproceedings{wu2018group,
title={Group Normalization},
author={Wu, Yuxin and He, Kaiming},
booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
year={2018}
}
```
## Results and Models
| 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) |
**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].
\ No newline at end of file
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