/mmdetection

Res2Net achieves SOTA on mmdetection.

Primary LanguagePythonApache License 2.0Apache-2.0

Res2Net for object detection and instance segmentation based on mmdetection.

News

Introduction

We propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer.

Backbone Params. GFLOPs top-1 err. top-5 err.
ResNet-101 44.6 M 7.8 22.63 6.44
ResNeXt-101-64x4d 83.5M 15.5 20.40 -
HRNetV2p-W48 77.5M 16.1 20.70 5.50
Res2Net-101 45.2M 8.3 18.77 4.64

Compared with other backbone networks, Res2Net requires fewer parameters and FLOPs.

Note:

  • GFLOPs for classification are calculated with image size (224x224).

Detection and segmentation Results

Faster R-CNN

Backbone Params. GFLOPs box AP
R-101-FPN 60.52M 283.14 39.4
X-101-64x4d-FPN 99.25M 440.36 41.3
HRNetV2p-W48 83.36M 459.66 41.5
Res2Net-101 61.18M 293.68 42.3

Mask R-CNN

Backbone Params. GFLOPs box AP mask AP
R-101-FPN 63.17M 351.65 40.3 36.5
X-101-64x4d-FPN 101.9M 508.87 42.0 37.7
HRNetV2p-W48 86.01M 528.17 42.9 38.3
Res2Net-101 63.83M 362.18 43.3 38.6

Cascade R-CNN

Backbone Params. GFLOPs box AP
R-101-FPN 88.16M 310.78 42.5
X-101-64x4d-FPN 126.89M 468.00 44.7
HRNetV2p-W48 111.00M 487.30 44.6
Res2Net-101 88.82M 321.32 45.5

Cascade Mask R-CNN

Backbone Params. GFLOPs box AP mask AP
R-101-FPN 96.09M 516.30 43.3 37.6
X-101-64x4d-FPN 134.82M 673.52 45.7 39.4
HRNetV2p-W48 118.93M 692.82 46.0 39.5
Res2Net-101 96.75M 526.84 46.1 39.4

Hybrid Task Cascade (HTC)

Backbone Params. GFLOPs box AP mask AP
R-101-FPN 99.03M 563.76 44.9 39.4
X-101-64x4d-FPN 137.75M 720.98 46.9 40.8
HRNetV2p-W48 121.87M 740.28 47.0 41.0
Res2Net-101 99.69M 574.30 47.5 41.3

Note:

  • GFLOPs are calculated with image size (1280, 800).
  • All detection methods in this page use pytorch style. Lr schd is 2x for Faster R-CNN and Mask R-CNN, and 20e for others.
  • Res2Net ImageNet pretrained models are in Res2Net-PretrainedModels.
  • More applications of Res2Net are in Res2Net-Github.

MMDetection

News: We released the technical report on ArXiv.

Documentation: https://mmdetection.readthedocs.io/

Introduction

The master branch works with PyTorch 1.1 to 1.4.

mmdetection is an open source object detection toolbox based on PyTorch. It is a part of the open-mmlab project developed by Multimedia Laboratory, CUHK.

demo image

Major features

  • Modular Design

    We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules.

  • Support of multiple frameworks out of box

    The toolbox directly supports popular and contemporary detection frameworks, e.g. Faster RCNN, Mask RCNN, RetinaNet, etc.

  • High efficiency

    All basic bbox and mask operations run on GPUs now. The training speed is faster than or comparable to other codebases, including Detectron, maskrcnn-benchmark and SimpleDet.

  • State of the art

    The toolbox stems from the codebase developed by the MMDet team, who won COCO Detection Challenge in 2018, and we keep pushing it forward.

Apart from MMDetection, we also released a library mmcv for computer vision research, which is heavily depended on by this toolbox.

License

This project is released under the Apache 2.0 license.

Changelog

v1.1.0 was released in 24/2/2020. Please refer to CHANGELOG.md for details and release history.

Benchmark and model zoo

Supported methods and backbones are shown in the below table. Results and models are available in the Model zoo.

ResNet ResNeXt SENet VGG HRNet Res2Net
RPN ✓ ✓ ☐ ✗ ✓ ✓
Fast R-CNN ✓ ✓ ☐ ✗ ✓ ✓
Faster R-CNN ✓ ✓ ☐ ✗ ✓ ✓
Mask R-CNN ✓ ✓ ☐ ✗ ✓ ✓
Cascade R-CNN ✓ ✓ ☐ ✗ ✓ ✓
Cascade Mask R-CNN ✓ ✓ ☐ ✗ ✓ ✓
SSD ✗ ✗ ✗ ✓ ✗ ✗
RetinaNet ✓ ✓ ☐ ✗ ✓ ✓
GHM ✓ ✓ ☐ ✗ ✓ ✓
Mask Scoring R-CNN ✓ ✓ ☐ ✗ ✓ ✓
Double-Head R-CNN ✓ ✓ ☐ ✗ ✓ ✓
Grid R-CNN (Plus) ✓ ✓ ☐ ✗ ✓ ✓
Hybrid Task Cascade ✓ ✓ ☐ ✗ ✓ ✓
Libra R-CNN ✓ ✓ ☐ ✗ ✓ ✓
Guided Anchoring ✓ ✓ ☐ ✗ ✓ ✓
FCOS ✓ ✓ ☐ ✗ ✓ ✓
RepPoints ✓ ✓ ☐ ✗ ✓ ✓
Foveabox ✓ ✓ ☐ ✗ ✓ ✓
FreeAnchor ✓ ✓ ☐ ✗ ✓ ✓
NAS-FPN ✓ ✓ ☐ ✗ ✓ ✓
ATSS ✓ ✓ ☐ ✗ ✓ ✓

Other features

Installation

Please refer to INSTALL.md for installation and dataset preparation.

Get Started

Please see GETTING_STARTED.md for the basic usage of MMDetection.

Contributing

We appreciate all contributions to improve MMDetection. Please refer to CONTRIBUTING.md for the contributing guideline.

Acknowledgement

MMDetection is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new detectors.

Citation

If you use this toolbox or benchmark in your research, please cite this project.

@article{mmdetection,
  title   = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark},
  author  = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and
             Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and
             Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and
             Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and
             Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong
             and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua},
  journal= {arXiv preprint arXiv:1906.07155},
  year={2019}
}

Contact

This repo is currently maintained by Kai Chen (@hellock), Yuhang Cao (@yhcao6), Wenwei Zhang (@ZwwWayne), Jiarui Xu (@xvjiarui). Other core developers include Jiangmiao Pang (@OceanPang) and Jiaqi Wang (@myownskyW7).