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https://github.com/bharatsingh430/soft-nms

Object Detection
https://github.com/bharatsingh430/soft-nms

coco detector pascal-voc

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Object Detection

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README 

        
# Soft-NMS



This repository includes the code for Soft-NMS. Soft-NMS is integrated with two object detectors, R-FCN and Faster-RCNN. The Soft-NMS paper can be found [here](https://arxiv.org/pdf/1704.04503.pdf).



Soft-NMS is complementary to multi-scale testing and iterative bounding box regression. Check [MSRA](http://presentations.cocodataset.org/COCO17-Detect-MSRA.pdf) slides from the COCO 2017 challenge. 



**8 out of top 15 submissions used Soft-NMS in the [COCO 2017 detection challenge](http://cocodataset.org/#detections-leaderboard)!.**



We are also making our ICCV [reviews](http://www.cs.umd.edu/~bharat/reviews.html) and our [rebuttal](http://www.cs.umd.edu/~bharat/rebuttal.html) public. This should help to clarify some concerns which you may have.



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](https://github.com/bharatsingh430/py-R-FCN-multiGPU/) for details about setting up object detection pipelines.

The Soft-NMS repository also contains code for training these detectors on multiple GPUs. **The position sensitive ROI Pooling layer is updated so that interpolation of bins is correct, like ROIAlign in Mask RCNN**. The COCO detection model for R-FCN can be found [here](https://drive.google.com/open?id=0B6T5quL13CdHX04xN1ZQX2IyMms). All other detection models used in the paper 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 minival     | 33.9% |

|R-FCN,  Soft-NMS L | COCO 2014 train+val -minival | COCO 2015 minival     | 34.8% |

|R-FCN,  Soft-NMS G | COCO 2014 train+val -minival | COCO 2015 minival     | 35.1% |

|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{

      Author = {Navaneeth Bodla and Bharat Singh and Rama Chellappa and Larry S. Davis},

      Title = {Soft-NMS -- Improving Object Detection With One Line of Code},

      Booktitle = {Proceedings of the IEEE International Conference on Computer Vision},

      Year = {2017}

    }