Chainer_Mask_R-CNN

Chainer implementation of Mask R-CNN - the multi-task network for object detection, object classification, and instance segmentation. (https://arxiv.org/abs/1703.06870)
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Examples

Requirements

• Chainer
• Chainercv
• Cupy
  (operable if your environment can run chainer > v3 with cuda and cudnn.)
  (verified as operable: chainer==3.1.0, chainercv==0.7.0, cupy==1.0.3)

$ pip install chainer   
$ pip install chainercv
$ pip install cupy

• Python 3.0+
• NumPy
• Matplotlib
• OpenCV

TODOs

• Precision Evaluator
• Feature Pyramid Network
• Pose Estimation

Prerequisite

• Download 'ResNet-50-model.caffemodel' from the "OneDrive download" of ResNet pretrained models for model initialization and place it in ~/.chainer/dataset/pfnet/chainer/models/

• COCO 2017 dataset : the COCO dataset can be downloaded and unzipped by:

bash getData.sh

Generate the list file by:

python utils/makecocolist.py

Setup the COCO API:

git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI/
make
python setup.py install
cd ../../

Train

python train.py 

arguments and the default conditions are defined as follows:

'--dataset', choices=('coco2017'), default='coco2017'   
'--extractor', choices=('resnet50','resnet101'), default='resnet50', help='extractor network'
'--gpu', '-g', type=int, default=0   
'--lr', '-l', type=float, default=1e-4   
'--batchsize', '-b', type=int, default=8   
'--out', '-o', default='result',  help='output directory'   
'--seed', '-s', type=int, default=0   
'--roialign', action='store_true', default=True, help='True: ROIAlign, False: ROIpooling'
'--step_size', '-ss', type=int, default=400000  
'--lr_step', '-ls', type=int, default=480000    
'--lr_initialchange', '-li', type=int, default=800     
'--pretrained', '-p', type=str, default='imagenet'   
'--snapshot', type=int, default=4000   
'--resume', type=str   
'--iteration', '-i', type=int, default=800000   
'--roi_size', '-r', type=int, default=7, help='ROI size for mask head input'
'--gamma', type=float, default=1, help='mask loss balancing factor'   

note that we use a subdivision-based updater to enable training with large batch size.

Demo

Segment the objects in the input image by executing:

python demo.py --image <input image> --modelfile result/snapshot_model.npz 

Citation

Please cite the original paper in your publications if it helps your research:

@article{DBLP:journals/corr/HeGDG17,
  author    = {Kaiming He and
              Georgia Gkioxari and
              Piotr Doll{\'{a}}r and
              Ross B. Girshick},
  title     = {Mask {R-CNN}},
  journal   = {CoRR},
  volume    = {abs/1703.06870},
  year      = {2017},
  url       = {http://arxiv.org/abs/1703.06870},
  archivePrefix = {arXiv},
  eprint    = {1703.06870},
  timestamp = {Wed, 07 Jun 2017 14:42:32 +0200},
  biburl    = {http://dblp.org/rec/bib/journals/corr/HeGDG17},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}