Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond
| Date | Stars |
|---|---|
| 2026-07-24 | 1220 |
| 2026-07-25 | 1220 |
| 2026-07-28 | 1220 |
| 2026-07-30 | 1220 |
| 2026-08-06 | 1220 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# GCNet for Object Detection
[](https://paperswithcode.com/sota/object-detection-on-coco-minival?p=global-context-networks)
[](https://paperswithcode.com/sota/instance-segmentation-on-coco-minival?p=global-context-networks)
[](https://paperswithcode.com/sota/object-detection-on-coco?p=global-context-networks)
[](https://paperswithcode.com/sota/instance-segmentation-on-coco?p=global-context-networks)
By [Yue Cao](http://yue-cao.me), [Jiarui Xu](http://jerryxu.net), [Stephen Lin](https://scholar.google.com/citations?user=c3PYmxUAAAAJ&hl=en), Fangyun Wei, [Han Hu](https://sites.google.com/site/hanhushomepage/).
This repo is a official implementation of ["GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond"](https://arxiv.org/abs/1904.11492) on COCO object detection based on open-mmlab's [mmdetection](https://github.com/open-mmlab/mmdetection). The core operator GC block could be find [here](https://github.com/xvjiarui/GCNet/blob/master/mmdet/ops/gcb/context_block.py).
Many thanks to mmdetection for their simple and clean framework.
*Update on 2020/12/07*
The extension of GCNet got accepted by TPAMI ([PDF](https://arxiv.org/pdf/2012.13375.pdf)).
*Update on 2019/10/28*
GCNet won the **Best Paper Award** at ICCV 2019 Neural Architects Workshop!
*Update on 2019/07/01*
The code is refactored.
More results are provided and all configs could be found in `configs/gcnet`.
**Notes**: Both PyTorch official SyncBN and Apex SyncBN have some stability issues.
During training, mAP may drops to zero and back to normal during last few epochs.
*Update on 2019/06/03*
GCNet is supported by the official mmdetection repo [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet).
Thanks again for open-mmlab's work on open source projects.
## Introduction
**GCNet** is initially described in [arxiv](https://arxiv.org/abs/1904.11492). Via absorbing advantages of Non-Local Networks (NLNet) and Squeeze-Excitation Networks (SENet), GCNet provides a simple, fast and effective approach for global context modeling, which generally outperforms both NLNet and SENet on major benchmarks for various recognition tasks.
## Citing GCNet
```
@article{cao2019GCNet,
title={GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond},
author={Cao, Yue and Xu, Jiarui and Lin, Stephen and Wei, Fangyun and Hu, Han},
journal={arXiv preprint arXiv:1904.11492},
year={2019}
}
```
## Main Results
### Results on R50-FPN with backbone (fixBN)
| Back-bone | Model | Back-bone Norm | Heads | Context | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download |
|:---------:|:----------------:|:-------------:|:----------------:|:--------------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:|
| R50-FPN | Mask | fixBN | 2fc (w/o BN) | - | 1x | 3.9 | 0.453 | 10.6 | 37.3 | 34.2 | [model](https://1drv.ms/u/s!AkEXj14LxwVpffUWWM4A0tFYYCk?e=IM6zgo)|
| R50-FPN | Mask | fixBN | 2fc (w/o BN) | GC(c3-c5, r16) | 1x | 4.5 | 0.533 | 10.1 | 38.5 | 35.1 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/models/gcnet/mask_rcnn_r16_gcb_c3-c5_r50_fpn_1x_20190602-c550c707.pth)|
| R50-FPN | Mask | fixBN | 2fc (wExcerpt of 15,415 characters
Read on GitHub
Kai Chen · @open-mmlab @internlm · China
339
Cao Yuhang · CUHK, MMLab · Hong Kong
75
Jiangmiao Pang · Shanghai AI Laboratory · China
40
Thang Vu · 42dot · South Korea
20
Jiaqi Wang · @open-mmlab · Hong Kong
18
Yue Cao · Sand.ai
13
Jerry Jiarui XU · UCSD
7
5
lizz · CUHK · Hong Kong
4
3
Vladislav Sovrasov · @opencv @intel · Germany
2
Donny You · Peking University & Alibaba · China
2
Patrick Liu
2
Zheng Zhang · Sand.AI · China
1
1
Christian Clauss · Christian Clauss · Switzerland
1
1
youkaichao · @vllm-project · China
1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:933b1f009bbebef5, topic:computer-vision, topic:object-detection, readme:object detection
matched fp:933b1f009bbebef5, topic:deep-learning