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SSM-Pytorch

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Towards Human-Machine Cooperation: Self-supervised Sample Mining for Object Detection

// repository documentation

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# SSM (the Unofficial Version of Pytorch Implementation)

**<a href="https://arxiv.org/pdf/1803.09867.pdf">Towards Human-Machine Cooperation: Self-supervised Sample Mining for Object Detection</a>**

<a href="http://kezewang.com">Keze Wang</a>,
<a href="https://yanxp.github.io/">Xiaopeng Yan</a>,
Dongyu Zhang,
<a href="http://www4.comp.polyu.edu.hk/~cslzhang/">Lei Zhang</a>,
<a href="http://www.linliang.net/">Liang Lin</a>

Sun Yat-Sen University, Presented at [CVPR2018](https://arxiv.org/pdf/1803.09867.pdf)

### License

For Academic Research Use Only!

### Strict Requirements

Python 3.6

OpenCV

PyTorch 0.3

Note: PyTorch 0.4 or Python 2.7 is not supported !

### Citing SSM

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

@inproceedings{wang18ssm,
        Author = {Keze Wang, Xiaopeng Yan, Dongyu Zhang, Lei Zhang, Liang Lin},
        Title = {{SSM}: Towards Human-Machine Cooperation: Self-supervised Sample Mining for Object Detection},
        Journal = {Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
        Year = {2018}
    }

### Dependencies

The code is built on top of https://github.com/ruotianluo/pytorch-faster-rcnn. Please carefully read through the pytorch-faster-rcnn instructions and make sure pytorch-faster-rcnn can run within your enviornment.

### Datasets/Pre-trained model

1. In our paper, we used Pascal VOC2007/VOC2012 and COCO as our datasets, and res101.pth model as our pre-trained model.

2. Please download ImageNet-pre-trained res101.pth model manually, and put them into $SSM_ROOT/data/imagenet_models

### Usage

1. training

Before training, please prepare your dataset and pre-trained model and store them in the right path as R-FCN.
You can go to ./tools/ and modify train_net.py to reset some parameters.Then, simply run sh ./train.sh.

2. testing

Before testing, you can modify test.sh to choose the trained model path, then simply run sh ./test.sh to get the evaluation result.

### Misc

Tested on Ubuntu 14.04 with a Titan X GPU (12G) and Intel(R) Xeon(R) CPU E5-2623 v3 @ 3.00GHz.

SSM-Pytorch

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