CP-Cluster
Confidence Propagation Cluster aims to replace NMS-based methods as a better box fusion framework in 2D/3D Object detection, Instance Segmentation:
[Confidence Propagation Cluster: Unleash the Full Potential of Object Detectors](arxivlink to do),
Yichun Shen*, Wanli Jiang*, Zhen Xu, Rundong Li, Junghyun Kwon, Siyi Li,
Contact: [email protected]. Welcome for any questions and comments!
Abstract
It’s been a long history that most object detection methods obtain objects by using the non-maximum suppression(NMS) and its improved versions like Soft-NMS to remove redundant bounding boxes. We challenge those NMS-based methods from three aspects: 1) The bounding box with highest confidence value may not be the true positive having the biggest overlap with the ground-truth box. 2) Not only suppression is required for redundant boxes, but also confidence enhancement is needed for those true positives. 3) Sorting candidate boxes by confidence values is not necessary so that full parallelism is achievable.
Inspired by belief propagation (BP), we propose the Confidence Propagation Cluster (CP-Cluster) to replace NMS-based methods, which is fully parallelizable as well as better in accuracy. In CP-Cluster, we borrow the message passing mechanism from BP to penalize redundant boxes and enhance true positives simultaneously in an iterative way until convergence. We verified the effectiveness of CP-Cluster by applying it to various mainstream detectors such as FasterRCNN, SSD, FCOS, YOLOv3, YOLOv5, Centernet etc. Experiments on MS COCO show that our plug and play method, without retraining detectors, is able to steadily improve average mAP of all those state-of-the-art models with a clear margin from 0.2 to 1.9 respectively when compared with NMS-based methods.
Highlights
-
Better accuracy: Compared with all previous NMS-based methods, CP-Cluster manages to achieve better accuracy
-
Fully parallelizable: No box sorting is required, and each candidate box can be handled separately when propagating confidence messages
Main results
Detectors from MMDetection on COCO val/test-dev
| Method | NMS | Soft-NMS | CP-Cluster |
|---|---|---|---|
| FRcnn-fpn50 | 38.4 / 38.7 | 39.0 / 39.2 | 39.2 / 39.4 |
| Yolov3 | 33.5 / 33.5 | 33.6 / 33.6 | 34.1 / 34.1 |
| Retina-fpn50 | 37.4 / 37.7 | 37.5 / 37.9 | 38.1 / 38.4 |
| FCOS-X101 | 42.7 / 42.8 | 42.7 / 42.8 | 42.9 / 43.1 |
| AutoAssign-fpn50 | 40.4 / 40.6 | 40.5 / 40.7 | 41.0 / 41.2 |
Yolov5(v6 model) on COCO val
| Model | NMS | Soft-NMS | CP-Cluster |
|---|---|---|---|
| Yolov5s | 37.2 | 37.4 | 37.5 |
| Yolov5m | 45.2 | 45.3 | 45.5 |
| Yolov5l | 48.8 | 48.8 | 49.1 |
| Yolov5x | 50.7 | 50.8 | 51.0 |
| Yolov5s_1280 | 44.5 | 50.8 | 44.8 |
| Yolov5m_1280 | 51.1 | 51.1 | 51.3 |
| Yolov5l_1280 | 53.6 | 53.7 | 53.8 |
| Yolov5x_1280 | 54.7 | 54.8 | 55.0 |
Replace maxpooling with CP-Cluster for Centernet(Evaluated on COCO test-dev), where "flip_scale" means flip and multi-scale augmentations
| Model | maxpool | Soft-NMS | CP-Cluster |
|---|---|---|---|
| dla34 | 37.3 | 38.1 | 39.2 |
| dla34_flip_scale | 41.7 | 40.6 | 43.3 |
| hg_104 | 40.2 | 40.6 | 41.1 |
| hg_104_flip_scale | 45.2 | 44.3 | 46.6 |
Instance Segmentation(MASK-RCNN, 3X models) from MMDetection on COCO test-dev
| Box/Mask AP | NMS | Soft-NMS | CP-Cluster |
|---|---|---|---|
| MRCNN_R50 | 41.5/37.7 | 42.0/37.8 | 42.1/38.0 |
| MRCNN_R101 | 43.1/38.8 | 43.6/39.0 | 43.6/39.1 |
| MRCNN_X101 | 44.6/40.0 | 45.2/40.2 | 45.2/40.2 |
Integrate into MMCV
Clone the mmcv repo from https://github.com/shenyi0220/mmcv (Cut down by 9/28/2021 from main branch with no extra modifications)
Copy the implementation of "cp_cluster_cpu" in src/nms.cpp to the mmcv nms code("mmcv/ops/csrc/pytorch/nms.cpp")
Borrow the "soft_nms_cpu" API by calling "cp_cluster_cpu" rather than orignal Soft-NMS implementations, so that modify the code like below:
@@ -186,8 +186,8 @@ Tensor softnms(Tensor boxes, Tensor scores, Tensor dets, float iou_threshold,
if (boxes.device().is_cuda()) {
AT_ERROR("softnms is not implemented on GPU");
} else {
- return softnms_cpu(boxes, scores, dets, iou_threshold, sigma, min_score,
- method, offset);
+ return cp_cluster_cpu(boxes, scores, dets, iou_threshold, min_score,
+ offset, 0.8, 3);
}
}
Compile mmcv with source code
MMCV_WITH_OPS=1 pip install -e .
Reproduce Object Detection and Instance Segmentation in MMDetection
Make sure that the MMCV with CP-Cluster has been successfully installed.
Download code from https://github.com/shenyi0220/mmdetection (Cut down by 9/26/2021 from main branch with some config file modifications to call Soft-NMS/CP-Cluster), and install all the dependancies accordingly.
Download models from model zoo
Run below command to reproduce Faster-RCNN-r50-fpn-2x:
python tools/test.py ./configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py ./checkpoints/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth --eval bbox
To check original metrics with NMS, you can switch the model config back to use default NMS.
To check Soft-NMS metrics, just re-compile with mmcv without CP-Cluster modifications.
Reproduce Yolov5
Make sure that the MMCV with CP-Cluster has been successfully installed.
Download code from https://github.com/shenyi0220/yolov5 (Cut down by 11/9/2021 from main branch, replacing the default torchvision.nms with CP-Cluster from mmcv), and install all the dependancies accordingly.
Run below command to reproduce the CP-Cluster exp with yolov5s-v6
python val.py --data coco.yaml --conf 0.001 --iou 0.6 --weights yolov5s.pt --batch-size 32
License
For the time being, this implementation is published with NVIDIA proprietary license, and the only usage of the source code is to reproduce the experiments of CP-Cluster. For any possible commercial use and redistribution of the code, pls contact [email protected]
Citation
If you find this project useful for your research, please use the following BibTeX entry.
@shenyi0220 非常感谢您的工作，我在复现CP-cluster在yolov5上测试coco数据集的过程中发现yolov5自带计算出来的
25 Oct 10, 2022
13 Nov 17, 2022
5 Sep 12, 2022