#SEC-tensorflow version

Introduction

This is a project which just move the SEC-caffe to SEC-tensorflow. The SEC is referring to the approach for weakly-supervised semantic segmentation in the paper "seed, expand and constrain: three principles for weakly-supervised image segmentation". And here, I just use the tensorflow to implement the approach with the help of the SEC-caffe project.

Citing this repository

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

@inproceedings{kolesnikov2016seed,

​ title={Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation},

​ author={Kolesnikov, Alexander and Lampert, Christoph H.},

​ booktitle={European Conference on Computer Vision ({ECCV})},

​ year={2016},

​ organization={Springer}

}

Preparation

for using this code, you have to do something else:

1. Install pydensecrf

For using the densecrf in python, we turn to the project pydensecrf. And you just using the following code to install it.

pip install pydensecrf

note: from the page of pydensecrf, maybe you should upgrade the cython to a newer version befor install it.

2. Download the data and model

1. for pascal data, please referring to its official website and to the augmental SBD data. Just download it and extract it in the data/, then 'cd data' and run convert.py with 'python convert.py'.
2. for localization_cues.pickle, please referring to SEC-caffe. And download it and extract in the data/ (don't forget to uncompress the data by "gzip -kd localization_cues.pickle.gz").
3. for the init.npy, I upload a converted file in google driver, just download it and put it in the model/ . And those weights in the file is exactly the same with the vgg16_20M.caffemodel in SEC-caffe.

For more details, you can referring to the correspond code files or leave a message in the issue.

3. Be careful about the versions of python and tensorflow

We just only test on python3.7 and tensorflow 1.14.0.

Training

then, you just input the following sentence to train it.

python SEC.py <gpu_id>

Evaluation

I just release a project to provide the code for evaluation.