- Res2Net is now one of the basic bonebones in MMDetection v2 framework https://github.com/open-mmlab/mmdetection. Using MMDetection v2 with Res2Net achieves better performance with less computational cost.
- Res2Net with mmdetection v2 is now available in https://github.com/Res2Net/mmdetection/tree/mmdet_res2net.
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).
| 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 |
| 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 |
| 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 |
| 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 |
| 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.
News: We released the technical report on ArXiv.
Documentation: https://mmdetection.readthedocs.io/
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.
-
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.
This project is released under the Apache 2.0 license.
v1.1.0 was released in 24/2/2020. Please refer to CHANGELOG.md for details and release history.
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
- CARAFE
- DCNv2
- Group Normalization
- Weight Standardization
- OHEM
- Soft-NMS
- Generalized Attention
- GCNet
- Mixed Precision (FP16) Training
- InstaBoost
Please refer to INSTALL.md for installation and dataset preparation.
Please see GETTING_STARTED.md for the basic usage of MMDetection.
We appreciate all contributions to improve MMDetection. Please refer to CONTRIBUTING.md for the contributing guideline.
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.
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}
}
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).