simple-IAM

A simple PyTorch implementation of Learning Instance Activation Maps for Weakly Supervised Instance Segmentation, in CVPR 2019

A simple implementation as my homework, modified based on ultra-thin-PRM.

Implementation details With my own understanding of the paper, it may be different from the author.

If you have any good suggestions, please let me know. Thank you !

Sample result

ps: Use dense CRF to generate predictions without adjusting the default parameters.

Weights and config

PRM_modules: https://drive.google.com/file/d/1L6czsneapAh_cX-rJpufT8V6wR-LfDxE/view?usp=sharing

Filling_modules: https://drive.google.com/file/d/1abHbPVftdyEP9lgnkx3ps2ok_5poVFtI/view?usp=sharing

config: https://drive.google.com/file/d/14vNYjj3ta8Edo9I3Pb8X16JRrfQ9FHOr/view?usp=sharing

simple-IAM
    ├── snapshots   
    │   ├── model_prm_latest.pth.tar
    │   ├── model_filling_latest.pth.tar
    │
    │── config.yml

Run demo

First download the shared weights and configuration and put them in the right place. Create a new directory out as the output location. You can also change the folder path by editing config.yml.

python main.py --run_demo=true

Train

Dataset：VOC2012

Download the PASCAL-VOC2012 dataset:

wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
tar xvf VOCtrainval_11-May-2012.tar

Configure config.yml to determine the location of the dataset.

Train PRM modules:

python main.py --train_prm=true

Train Filling modules:

Download the proposals of VOC2012.

https://drive.google.com/file/d/1XOMxl89Mp6GzzYy8dBoQhsx04JY3XIU3/view?usp=sharing

Rename directory to ImageProposals after unzip and put it in the position shown below.

VOC2012
    ├── Annotations 
    ├── ImageProposals
    ├── ImageSets
    ├── JPEGImages
    ├── SegmentationClass
    ├── SegmentationObject

then:

python main.py --train_filling=true

If you want to continue the previous training, these two parameters may be helpful.

--train_prm_resume=true and --train_filling_resume=true

Inference

Currently only inferences similar to VOC2012 structure are supported.

Configure config.yml to determine the location of the test dataset.

python main.py --run_demo=true

Reference

@article{Zhu2019IAM,
    title={{Learning Instance Activation Maps for Weakly Supervised Instance Segmentation}},
    author={Zhu, Y. and Zhou, Y. and Xu, H. and Ye, Q. and Doermann, D. and Jiao, J.},
    booktitle={CVPR},
    year={2019}
}