Chainer implementation of Mask R-CNN - the multi-task network for object detection, object classification, and instance segmentation.
(https://arxiv.org/abs/1703.06870)
日本語版 README
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- 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
- Precision Evaluator
- Feature Pyramid Network
- Pose Estimation
-
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 ../../
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.
Segment the objects in the input image by executing:
python demo.py --image <input image> --modelfile result/snapshot_model.npz
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}
}
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