This repository is currently being prepared.
This is the pytorch implementation of the ECCV 2016 paper 'Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation' (paper).
The implementation heavily refers to the python implementation of DeepLab-ResNet (isht7/pytorch-deeplab-resnet) and the public code of Seed, Expand, Constrain (https://github.com/kolesman/SEC).
- Python packages:
$ pip install -r python-dependencies.txt
$ conda install -c conda-forge opencv
$ conda install -c conda-forge tensorboardx
If you have an issue with numpy.core.multiarray, remove the currently installed numpy from your virtual environment and re-install with the follwing line:
$ pip install -U numpy
- Build the Fully connected CRF wrapper:
Install the Eigen3 package and link the installed custum Eigen3 folder to '/usr/local/include/Eigen'. Then
$ pip install CRF/
- Install PyTorch.
$ conda install pytorch=0.4.1 torchvision cuda80 -c pytorch
- Prepare the initial vgg16 model pretrained on ImageNet.
$ mkdir vgg16_20M
- Prepare localization cues.
$ cd localization_cues
$ gzip -kd localization_cues/localization_cues.pickle.gz
- Prepare dataset (e.g., PASCAL VOC 2012) and update the directory in train.py.
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