Mask R-CNN
Introduction
@article{He_2017,
title={Mask R-CNN},
journal={2017 IEEE International Conference on Computer Vision (ICCV)},
publisher={IEEE},
author={He, Kaiming and Gkioxari, Georgia and Dollar, Piotr and Girshick, Ross},
year={2017},
month={Oct}
}
Results and models
| Backbone |
Style |
Lr schd |
Mem (GB) |
Inf time (fps) |
box AP |
mask AP |
Download |
| R-50-FPN |
caffe |
1x |
4.3 |
|
38.0 |
34.4 |
model | log |
| R-50-FPN |
pytorch |
1x |
4.4 |
16.1 |
38.2 |
34.7 |
model | log |
| R-50-FPN |
pytorch |
2x |
- |
- |
39.2 |
35.4 |
model | log |
| R-101-FPN |
caffe |
1x |
|
|
40.4 |
36.4 |
model | log |
| R-101-FPN |
pytorch |
1x |
6.4 |
13.5 |
40.0 |
36.1 |
model | log |
| R-101-FPN |
pytorch |
2x |
- |
- |
40.8 |
36.6 |
model | log |
| X-101-32x4d-FPN |
pytorch |
1x |
7.6 |
11.3 |
41.9 |
37.5 |
model | log |
| X-101-32x4d-FPN |
pytorch |
2x |
- |
- |
42.2 |
37.8 |
model | log |
| X-101-64x4d-FPN |
pytorch |
1x |
10.7 |
8.0 |
42.8 |
38.4 |
model | log |
| X-101-64x4d-FPN |
pytorch |
2x |
- |
- |
42.7 |
38.1 |
model | log |
| X-101-32x8d-FPN |
pytorch |
1x |
- |
- |
42.8 |
38.3 |
|
Pre-trained Models
We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks.