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) |
Inf time (fps) |
box AP |
mask AP |
Download |
R-50-FPN |
Faster |
pytorch |
dconv(c3-c5) |
- |
1x |
4.0 |
17.8 |
41.3 |
|
model | log |
R-50-FPN |
Faster |
pytorch |
mdconv(c3-c5) |
- |
1x |
4.1 |
17.6 |
41.4 |
|
model | log |
*R-50-FPN (dg=4) |
Faster |
pytorch |
mdconv(c3-c5) |
- |
1x |
4.2 |
17.4 |
41.5 |
|
model | log |
R-50-FPN |
Faster |
pytorch |
- |
dpool |
1x |
5.0 |
17.2 |
38.9 |
|
model | log |
R-50-FPN |
Faster |
pytorch |
- |
mdpool |
1x |
5.8 |
16.6 |
38.7 |
|
model | log |
R-101-FPN |
Faster |
pytorch |
dconv(c3-c5) |
- |
1x |
6.0 |
12.5 |
42.7 |
|
model | log |
X-101-32x4d-FPN |
Faster |
pytorch |
dconv(c3-c5) |
- |
1x |
7.3 |
10.0 |
44.5 |
|
model | log |
R-50-FPN |
Mask |
pytorch |
dconv(c3-c5) |
- |
1x |
4.5 |
15.4 |
41.8 |
37.4 |
model | log |
R-50-FPN |
Mask |
pytorch |
mdconv(c3-c5) |
- |
1x |
4.5 |
15.1 |
41.5 |
37.1 |
model | log |
R-101-FPN |
Mask |
pytorch |
dconv(c3-c5) |
- |
1x |
6.5 |
11.7 |
43.5 |
38.9 |
model | log |
R-50-FPN |
Cascade |
pytorch |
dconv(c3-c5) |
- |
1x |
4.5 |
14.6 |
43.8 |
|
model | log |
R-101-FPN |
Cascade |
pytorch |
dconv(c3-c5) |
- |
1x |
6.4 |
11.0 |
45.0 |
|
model | log |
R-50-FPN |
Cascade Mask |
pytorch |
dconv(c3-c5) |
- |
1x |
6.0 |
10.0 |
44.4 |
38.6 |
model | log |
R-101-FPN |
Cascade Mask |
pytorch |
dconv(c3-c5) |
- |
1x |
8.0 |
8.6 |
45.8 |
39.7 |
model | log |
X-101-32x4d-FPN |
Cascade Mask |
pytorch |
dconv(c3-c5) |
- |
1x |
9.2 |
|
47.3 |
41.1 |
model | log |
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.
- (*) For R-50-FPN (dg=4), dg is short for deformable_group. This model is trained and tested on Amazon EC2 p3dn.24xlarge instance.
- Memory, Train/Inf time is outdated.