OpenMMLab Detection Toolbox and Benchmark
https://mmdetection.readthedocs.io/
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
58 lines
8.1 KiB
58 lines
8.1 KiB
# Projects based on MMDetection |
|
|
|
There are many projects built upon MMDetection. |
|
We list some of them as examples of how to extend MMDetection for your own projects. |
|
As the page might not be completed, please feel free to create a PR to update this page. |
|
|
|
## Projects as an extension |
|
|
|
Some projects extend the boundary of MMDetection for deployment or other research fields. |
|
They reveal the potential of what MMDetection can do. We list several of them as below. |
|
|
|
- [OTEDetection](https://github.com/opencv/mmdetection): OpenVINO training extensions for object detection. |
|
- [MMDetection3d](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection. |
|
|
|
## Projects of papers |
|
|
|
There are also projects released with papers. |
|
Some of the papers are published in top-tier conferences (CVPR, ICCV, and ECCV), the others are also highly influential. |
|
To make this list also a reference for the community to develop and compare new object detection algorithms, we list them following the time order of top-tier conferences. |
|
Methods already supported and maintained by MMDetection are not listed. |
|
|
|
- Involution: Inverting the Inherence of Convolution for Visual Recognition, CVPR21. [[paper]](https://arxiv.org/abs/2103.06255)[[github]](https://github.com/d-li14/involution) |
|
- Multiple Instance Active Learning for Object Detection, CVPR 2021. [[paper]](https://openaccess.thecvf.com/content/CVPR2021/papers/Yuan_Multiple_Instance_Active_Learning_for_Object_Detection_CVPR_2021_paper.pdf)[[github]](https://github.com/yuantn/MI-AOD) |
|
- Adaptive Class Suppression Loss for Long-Tail Object Detection, CVPR 2021. [[paper]](https://arxiv.org/abs/2104.00885)[[github]](https://github.com/CASIA-IVA-Lab/ACSL) |
|
- Generalizable Pedestrian Detection: The Elephant In The Room, CVPR2021. [[paper]](https://arxiv.org/abs/2003.08799)[[github]](https://github.com/hasanirtiza/Pedestron) |
|
- Group Fisher Pruning for Practical Network Compression, ICML2021. [[paper]](https://github.com/jshilong/FisherPruning/blob/main/resources/paper.pdf)[[github]](https://github.com/jshilong/FisherPruning) |
|
- Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax, CVPR2020. [[paper]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Overcoming_Classifier_Imbalance_for_Long-Tail_Object_Detection_With_Balanced_Group_CVPR_2020_paper.pdf)[[github]](https://github.com/FishYuLi/BalancedGroupSoftmax) |
|
- Coherent Reconstruction of Multiple Humans from a Single Image, CVPR2020. [[paper]](https://jiangwenpl.github.io/multiperson/)[[github]](https://github.com/JiangWenPL/multiperson) |
|
- Look-into-Object: Self-supervised Structure Modeling for Object Recognition, CVPR 2020. [[paper]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_Look-Into-Object_Self-Supervised_Structure_Modeling_for_Object_Recognition_CVPR_2020_paper.pdf)[[github]](https://github.com/JDAI-CV/LIO) |
|
- Video Panoptic Segmentation, CVPR2020. [[paper]](https://arxiv.org/abs/2006.11339)[[github]](https://github.com/mcahny/vps) |
|
- D2Det: Towards High Quality Object Detection and Instance Segmentation, CVPR2020. [[paper]](http://openaccess.thecvf.com/content_CVPR_2020/html/Cao_D2Det_Towards_High_Quality_Object_Detection_and_Instance_Segmentation_CVPR_2020_paper.html)[[github]](https://github.com/JialeCao001/D2Det) |
|
- CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection, CVPR2020. [[paper]](https://arxiv.org/abs/2003.09119)[[github]](https://github.com/KiveeDong/CentripetalNet) |
|
- Learning a Unified Sample Weighting Network for Object Detection, CVPR 2020. [[paper]](http://openaccess.thecvf.com/content_CVPR_2020/html/Cai_Learning_a_Unified_Sample_Weighting_Network_for_Object_Detection_CVPR_2020_paper.html)[[github]](https://github.com/caiqi/sample-weighting-network) |
|
- Scale-equalizing Pyramid Convolution for Object Detection, CVPR2020. [[paper]](https://arxiv.org/abs/2005.03101) [[github]](https://github.com/jshilong/SEPC) |
|
- Revisiting the Sibling Head in Object Detector, CVPR2020. [[paper]](https://arxiv.org/abs/2003.07540)[[github]](https://github.com/Sense-X/TSD) |
|
- PolarMask: Single Shot Instance Segmentation with Polar Representation, CVPR2020. [[paper]](https://arxiv.org/abs/1909.13226)[[github]](https://github.com/xieenze/PolarMask) |
|
- Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection, CVPR2020. [[paper]](https://arxiv.org/abs/2003.11818)[[github]](https://github.com/ggjy/HitDet.pytorch) |
|
- ZeroQ: A Novel Zero Shot Quantization Framework, CVPR2020. [[paper]](https://arxiv.org/abs/2001.00281)[[github]](https://github.com/amirgholami/ZeroQ) |
|
- CBNet: A Novel Composite Backbone Network Architecture for Object Detection, AAAI2020. [[paper]](https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuY.1833.pdf)[[github]](https://github.com/VDIGPKU/CBNet) |
|
- RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation, AAAI2020. [[paper]](https://arxiv.org/abs/1912.05070)[[github]](https://github.com/wangsr126/RDSNet) |
|
- Training-Time-Friendly Network for Real-Time Object Detection, AAAI2020. [[paper]](https://arxiv.org/abs/1909.00700)[[github]](https://github.com/ZJULearning/ttfnet) |
|
- Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution, NeurIPS 2019. [[paper]](https://arxiv.org/abs/1909.06720)[[github]](https://github.com/thangvubk/Cascade-RPN) |
|
- Reasoning R-CNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection, CVPR2019. [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Xu_Reasoning-RCNN_Unifying_Adaptive_Global_Reasoning_Into_Large-Scale_Object_Detection_CVPR_2019_paper.pdf)[[github]](https://github.com/chanyn/Reasoning-RCNN) |
|
- Learning RoI Transformer for Oriented Object Detection in Aerial Images, CVPR2019. [[paper]](https://arxiv.org/abs/1812.00155)[[github]](https://github.com/dingjiansw101/AerialDetection) |
|
- SOLO: Segmenting Objects by Locations. [[paper]](https://arxiv.org/abs/1912.04488)[[github]](https://github.com/WXinlong/SOLO) |
|
- SOLOv2: Dynamic, Faster and Stronger. [[paper]](https://arxiv.org/abs/2003.10152)[[github]](https://github.com/WXinlong/SOLO) |
|
- Dense Peppoints: Representing Visual Objects with Dense Point Sets. [[paper]](https://arxiv.org/abs/1912.11473)[[github]](https://github.com/justimyhxu/Dense-RepPoints) |
|
- IterDet: Iterative Scheme for Object Detection in Crowded Environments. [[paper]](https://arxiv.org/abs/2005.05708)[[github]](https://github.com/saic-vul/iterdet) |
|
- Cross-Iteration Batch Normalization. [[paper]](https://arxiv.org/abs/2002.05712)[[github]](https://github.com/Howal/Cross-iterationBatchNorm) |
|
- A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection, NeurIPS2020 [[paper]](https://arxiv.org/abs/2009.13592)[[github]](https://github.com/kemaloksuz/aLRPLoss) |
|
- RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder, NeurIPS2020 [[paper]](https://arxiv.org/abs/2010.15831)[[github]](https://github.com/microsoft/RelationNet2) |
|
- Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection, CVPR2021[[paper]](https://arxiv.org/abs/2011.12885)[[github]](https://github.com/implus/GFocalV2) |
|
- Instances as Queries, ICCV2021[[paper]](http://arxiv.org/abs/2105.01928)[[github]](https://github.com/hustvl/QueryInst) |
|
- Swin Transformer: Hierarchical Vision Transformer using Shifted Windows, ICCV2021[[paper]](https://arxiv.org/abs/2103.14030)[[github]](https://github.com/SwinTransformer/) |
|
- Focal Transformer: Focal Self-attention for Local-Global Interactions in Vision Transformers, NeurIPS2021[[paper]](https://arxiv.org/abs/2107.00641)[[github]](https://github.com/microsoft/Focal-Transformer) |
|
- End-to-End Semi-Supervised Object Detection with Soft Teacher, ICCV2021[[paper]](https://arxiv.org/abs/2106.09018)[[github]](https://github.com/microsoft/SoftTeacher) |
|
- CBNetV2: A Novel Composite Backbone Network Architecture for Object Detection [[paper]](http://arxiv.org/abs/2107.00420)[[github]](https://github.com/VDIGPKU/CBNetV2) |
|
- Instances as Queries, ICCV2021 [[paper]](https://openaccess.thecvf.com/content/ICCV2021/papers/Fang_Instances_As_Queries_ICCV_2021_paper.pdf)[[github]](https://github.com/hustvl/QueryInst)
|
|
|