Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Introduction
@article{Ren_2017,
title={Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
publisher={Institute of Electrical and Electronics Engineers (IEEE)},
author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
year={2017},
month={Jun},
}
Results and models
Backbone |
Style |
Lr schd |
Mem (GB) |
Inf time (fps) |
box AP |
Download |
R-50-DC5 |
caffe |
1x |
- |
- |
37.2 |
model | log |
R-50-FPN |
caffe |
1x |
3.8 |
|
37.8 |
model | log |
R-50-FPN |
pytorch |
1x |
4.0 |
21.4 |
37.4 |
model | log |
R-50-FPN |
pytorch |
2x |
- |
- |
38.4 |
model | log |
R-101-FPN |
caffe |
1x |
5.7 |
|
39.8 |
model | log |
R-101-FPN |
pytorch |
1x |
6.0 |
15.6 |
39.4 |
model | log |
R-101-FPN |
pytorch |
2x |
- |
- |
39.8 |
model | log |
X-101-32x4d-FPN |
pytorch |
1x |
7.2 |
13.8 |
41.2 |
model | log |
X-101-32x4d-FPN |
pytorch |
2x |
- |
- |
41.2 |
model | log |
X-101-64x4d-FPN |
pytorch |
1x |
10.3 |
9.4 |
42.1 |
model | log |
X-101-64x4d-FPN |
pytorch |
2x |
- |
- |
41.6 |
model | log |
Different regression loss
We trained with R-50-FPN pytorch style backbone for 1x schedule.
Backbone |
Loss type |
Mem (GB) |
Inf time (fps) |
box AP |
Download |
R-50-FPN |
L1Loss |
4.0 |
21.4 |
37.4 |
model | log |
R-50-FPN |
IoULoss |
|
|
37.9 |
model | log |
R-50-FPN |
GIoULoss |
|
|
37.6 |
model | log |
R-50-FPN |
BoundedIoULoss |
|
|
37.4 |
model | log |
Pre-trained Models
We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks.