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, Yichun Shen*, Wanli Jiang*, Zhen Xu, Rundong Li, Junghyun Kwon, Siyi Li,

Contact: ashen@nvidia.com. 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	44.6	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/mmcv/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 CP-Cluster 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 CP-Cluster exps with 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

Reproduce CP-Cluster exps with Centernet

Clone the Centernet repo from https://github.com/shenyi0220/centernet-cp-cluster (Added CP-Cluster compatible utilities)

Prepare and configure the env according to https://github.com/shenyi0220/centernet-cp-cluster/blob/main/readme/INSTALL.md (Similar to original repo), suggesting Pytorch 1.7

Copy the CP-Cluster implementation("def cp_cluster") from "src/centernet/nms.pyx" to the centernet nms source file("src/lib/external/nms.pyx"), replacing the below APIs:

def cp_cluster(np.ndarray[float, ndim=2] boxes, float Nt=0.5, float threshold=0.01,
               int opt_sna=0, float wfa_threshold=0.8, int opt_sai=0):
    return soft_nms(boxes, 0.5, Nt, threshold, 1)

Compile the nms lib with below command:

cd src/lib/external
make

Hourglass model

python test.py ctdet --exp_id coco_hourglass_bp --arch hourglass --keep_res --nms --pre_cluster_method empty --filter_threshold 0.05 --nms_opt_sna 1 --nms_sna_threshold 0.8 --load_model ../models/ctdet_coco_hg.pth

Hourglass model with flip and multi-scale

python test.py ctdet --exp_id coco_hourglass_bp --arch hourglass --keep_res --nms --pre_cluster_method empty --filter_threshold 0.05 --nms_opt_sna 1 --nms_sna_threshold 0.8 --load_model ../models/ctdet_coco_hg.pth --flip_test --test_scales 0.5,0.75,1,1.25,1.5

DLA-34 model

python test.py ctdet --exp_id coco_dla_exp1 --arch hourglass --keep_res --nms --pre_cluster_method empty --filter_threshold 0.05 --nms_opt_sna 1 --nms_sna_threshold 0.8 --load_model ../models/ctdet_coco_dla_2x.pth

DLA-34 model with flip and multi-scale

python test.py ctdet --exp_id coco_dla_exp1 --arch hourglass --keep_res --nms --pre_cluster_method empty --filter_threshold 0.05 --nms_opt_sna 1 --nms_sna_threshold 0.8 --load_model ../models/ctdet_coco_dla_2x.pth --flip_test --test_scales 0.5,0.75,1,1.25,1.5

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 ashen@nvidia.com

Open Source Limitation

Due to proprietary and patent limitations, for the time being, only CPU implementation of CP-Cluster is open sourced. Full GPU-implementation and looser open source license are in application process.

Citation

If you find this project useful for your research, please use the following BibTeX entry.

@inproceedings{yichun2021cpcluster,
  title={Confidence Propagation Cluster: Unleash Full Potential of Object Detectors},
  author={Yichun Shen, Wanli Jiang, Zhen Xu, Rundong Li, Junghyun Kwon, Siyi Li},
  booktitle={arXiv preprint arXiv:2112.00342},
  year={2021}
}