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 | Config | Download |
|---|---|---|---|---|---|---|---|
| R-50-DC5 | caffe | 1x | - | - | 37.2 | config | model | log |
| R-50-FPN | caffe | 1x | 3.8 | 37.8 | config | model | log | |
| R-50-FPN | pytorch | 1x | 4.0 | 21.4 | 37.4 | config | model | log |
| R-50-FPN | pytorch | 2x | - | - | 38.4 | config | model | log |
| R-101-FPN | caffe | 1x | 5.7 | 39.8 | config | model | log | |
| R-101-FPN | pytorch | 1x | 6.0 | 15.6 | 39.4 | config | model | log |
| R-101-FPN | pytorch | 2x | - | - | 39.8 | config | model | log |
| X-101-32x4d-FPN | pytorch | 1x | 7.2 | 13.8 | 41.2 | config | model | log |
| X-101-32x4d-FPN | pytorch | 2x | - | - | 41.2 | config | model | log |
| X-101-64x4d-FPN | pytorch | 1x | 10.3 | 9.4 | 42.1 | config | model | log |
| X-101-64x4d-FPN | pytorch | 2x | - | - | 41.6 | config | 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 | Config | Download |
|---|---|---|---|---|---|---|
| R-50-FPN | L1Loss | 4.0 | 21.4 | 37.4 | config | model | log |
| R-50-FPN | IoULoss | 37.9 | config | model | log | ||
| R-50-FPN | GIoULoss | 37.6 | config | model | log | ||
| R-50-FPN | BoundedIoULoss | 37.4 | config | 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.
| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
|---|---|---|---|---|---|---|---|
| R-50-DC5 | caffe | 1x | - | 37.4 | config | model | log | |
| R-50-DC5 | caffe | 3x | - | 38.7 | config | model | log | |
| R-50-FPN | caffe | 2x | 4.3 | 39.7 | config | model | log | |
| R-50-FPN | caffe | 3x | 4.3 | 40.2 | config | model | log |
We further finetune some pre-trained models on the COCO subsets, which only contain only a few of the 80 categories.
| Backbone | Style | Class name | Pre-traind model | Mem (GB) | box AP | Config | Download |
|---|---|---|---|---|---|---|---|
| R-50-FPN | caffe | person | R-50-FPN-Caffe-3x | 3.7 | 55.8 | config | model | log |
| R-50-FPN | caffe | person-bicycle-car | R-50-FPN-Caffe-3x | 3.7 | 44.1 | config | model | log |