FCOS: Fully Convolutional One-Stage Object Detection
Introduction
@article{tian2019fcos,
title={FCOS: Fully Convolutional One-Stage Object Detection},
author={Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong},
journal={arXiv preprint arXiv:1904.01355},
year={2019}
}
Results and Models
| Backbone |
Style |
GN |
MS train |
Lr schd |
Mem (GB) |
Train time (s/iter) |
Inf time (fps) |
box AP |
Download |
| R-50 |
caffe |
N |
N |
1x |
5.5 |
0.373 |
13.7 |
35.7 |
model |
| R-50 |
caffe |
Y |
N |
1x |
6.9 |
0.396 |
13.6 |
36.7 |
model |
| R-50 |
caffe |
Y |
N |
2x |
- |
- |
- |
36.9 |
model |
| R-101 |
caffe |
Y |
N |
1x |
10.4 |
0.558 |
11.6 |
39.1 |
model |
| R-101 |
caffe |
Y |
N |
2x |
- |
- |
- |
39.1 |
model |
| Backbone |
Style |
GN |
MS train |
Lr schd |
Mem (GB) |
Train time (s/iter) |
Inf time (fps) |
box AP |
Download |
| R-50 |
caffe |
Y |
Y |
2x |
- |
- |
- |
38.7 |
model |
| R-101 |
caffe |
Y |
Y |
2x |
- |
- |
- |
40.8 |
model |
| X-101 |
caffe |
Y |
Y |
2x |
9.7 |
0.892 |
7.0 |
42.8 |
model |
Notes:
- To be consistent with the author's implementation, we use 4 GPUs with 4 images/GPU for R-50 and R-101 models, and 8 GPUs with 2 image/GPU for X-101 models.
- The X-101 backbone is X-101-64x4d.