Soft-NMS

This repository includes the code for Soft-NMS. Soft-NMS is integrated with two object detectors, R-FCN and Faster-RCNN. Soft-NMS paper can be found here.

To test the models with soft-NMS, clone the project and test your models as in standard object detection pipelines. This repository supports Faster-RCNN and R-FCN where an additional flag can be used for soft-NMS.

The flags are as follows,

1. Standard NMS. Use flag TEST.SOFT_NMS 0
2. Soft-NMS with linear weighting. Use flag TEST.SOFT_NMS 1 (this is the default option)
3. Soft-NMS with Gaussian weighting. Use flag TEST.SOFT_NMS 2

In addition, you can specify the sigma parameter for Gaussian weighting and the threshold parameter for linear weighting. Detections below 0.001 are discarded. For integrating soft-NMS in your code, refer to cpu_soft_nms function in lib/nms/cpu_nms.pyx and soft_nms wrapper function in lib/fast_rcnn/nms_wrapper.py. You can also implement your own weighting function in this file.

For testing a model on COCO or PASCAL, use the following script

./tools/test_net.py --gpu ${GPU_ID} \
  --def models/${PT_DIR}/${NET}/rfcn_end2end/test_agnostic.prototxt \
  --net ${NET_FINAL} \
  --imdb ${TEST_IMDB} \
  --cfg experiments/cfgs/rfcn_end2end_ohem_${PT_DIR}.yml \
  --set TEST.SOFT_NMS 1 # performs soft-NMS with linear weighting
  ${EXTRA_ARGS}

GPU_ID is the GPU you want to test on

NET_FINAL is the caffe-model to use

PT_DIR in {pascal_voc, coco} is the dataset directory

DATASET in {pascal_voc, coco} is the dataset to use

TEST_IMDB in {voc_0712_test,coco_2014_minival,coco_2014_test} is the test imdb

TEST.SOFT_NMS in {0,1,2} is flag for different NMS algorithms. 0 is standard NMS, 1 performs soft-NMS with linear weighting and 2 performs soft-NMS with gaussian weighting

Please refer to py-R-FCN-multiGPU for details about setting up object detection pipelines. This repository also contains code for training these detectors on multiple GPUs. The COCO detection model for R-FCN can be found here. All other detection models are publicly available.

Results on MS-COCO

	training data	test data	mAP@[0.5:0.95]
R-FCN, NMS	COCO 2014 train+val -minival	COCO 2015 test-dev	31.1%
R-FCN, Soft-NMS L	COCO 2014 train+val -minival	COCO 2015 test-dev	32.2%
R-FCN, Soft-NMS G	COCO 2014 train+val -minival	COCO 2015 test-dev	32.4%
F-RCNN, NMS	COCO 2014 train+val -minival	COCO 2015 test-dev	24.4%
F-RCNN, Soft-NMS L	COCO 2014 train+val -minival	COCO 2015 test-dev	25.5%
F-RCNN, Soft-NMS G	COCO 2014 train+val -minival	COCO 2015 test-dev	25.5%

R-FCN uses ResNet-101 as the backbone CNN architecture, while Faster-RCNN is based on VGG16.

Citing Soft-NMS

If you find this repository useful in your research, please consider citing:

@article{1704.04503,
  Author = {Navaneeth Bodla and Bharat Singh and Rama Chellappa and Larry S. Davis},
  Title = {Improving Object Detection With One Line of Code},
  Journal = {arXiv preprint arXiv:1704.04503},
  Year = {2017}
}