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)
日本語版 README
Training result for R-50-C4 model has been evaluated!
COCO box AP = 0.346 using our trainer (0.355 with official boxes)
COCO mask AP = 0.287 using our trainer (0.314 with official boxes)
to be updated
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 (bbox, COCO metric)
Detectron Model Parser
Modify ROIAlign
Mask inference using refined ROIs
Precision Evaluator (mask, COCO metric)
Improve segmentation AP for R-50-C4 model
Feature Pyramid Network (R-50-FPN)
Keypoint Detection (R-50-FPN, Keypoints)
Box AP 50:95 | Segm AP 50:95 | |
Ours (1 GPU) | 0.346 | 0.287 |
Detectron model | 0.350 | 0.295 |
Detectron caffe2 | 0.355 | 0.314 |
Download the pretrained model from the [Model Zoo] (https://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md)
(model
link of R-50-C4 Mask
at End-to-End Faster & Mask R-CNN Baselines
)
Make modelfiles
directory and put the downloaded file model_final.pkl
in it
Execute:
python utils/detectron_parser.py
And the converted model file is saved in modelfiles
Run the demo:
python demo.py --bn2affine --modelfile modelfiles/e2e_mask_rcnn_R-50-C4_1x_d2c.npz --image <input image>
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 getcoco.sh
Setup the COCO API:
git clone https://github.com/waleedka/coco cd coco/PythonAPI/ make python setup.py install cd ../../
note: the official coco repository is not python3 compatible.
Use the repository above in order to run our evaluation.
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 '--freeze_bn', action='store_true', default=False, help='freeze batchnorm gamma/beta' '--bn2affine', action='store_true', default=False, help='batchnorm to affine' '--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 '--validation', type=int, default=30000 '--resume', type=str '--iteration', '-i', type=int, default=800000 '--roi_size', '-r', type=int, default=14, 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 --contour
Evaluate the trained model with COCO metric (bounding box, segmentation) :
python train.py --lr 0 --iteration 1 --validation 1 --resume <trained_model>
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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