# SSM-Pytorch

**Repository Path**: tju_hfut_sym/SSM-Pytorch

## Basic Information

- **Project Name**: SSM-Pytorch
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-12-30
- **Last Updated**: 2021-12-30

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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)	

<p align=center><img width="80%" src="tools/ssm.png"/></p>


### 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.