Chainer Implementation of Mask R-CNN.
Project description
chainer-mask-rcnn
Chainer Implementation of Mask R-CNN.
Features
- ResNet50, ResNet101 backbone.
- VOC and COCO training examples.
- Reproduced result of original work (ResNet50, COCO).
- Weight copy from pretrained model at facebookresearch/Detectron.
- Training with batch size >= 2.
- Support FPN backbones.
- Keypoint detection.
Fig 1. Mask R-CNN, ResNet50, 8GPU, Ours, COCO 31.4 mAP@50:95
COCO Results
| Model | Implementation | N gpu training | mAP@50:95 | Log |
|---|---|---|---|---|
| Mask R-CNN, ResNet50 | Ours | 8 | 31.5 - 31.8 | Log |
| Mask R-CNN, ResNet50 | Detectron | 8 | 31.4 (30.8 after copied) | Log |
| FCIS, ResNet50 | FCIS | 8 | 27.1 | - |
Inference
# you can use your trained model
./demo.py logs/<YOUR_TRAINING_LOG> --img <IMAGE_PATH_OR_URL>
# COCO Example: Mask R-CNN, ResNet50, 31.4 mAP@50:95
cd examples/coco
LOG_DIR=logs/20180730_081433
mkdir -p $LOG_DIR
pip install gdown
gdown https://drive.google.com/uc?id=1XC-Mx4HX0YBIy0Fbp59EjJFOF7a3XK0R -O $LOG_DIR/snapshot_model.npz
gdown https://drive.google.com/uc?id=1fXHanL2pBakbkv83wn69QhI6nM6KjrzL -O $LOG_DIR/params.yaml
./demo.py $LOG_DIR
# copy weight from caffe2 to chainer
cd examples/coco
./convert_caffe2_to_chainer.py # or download from https://drive.google.com/open?id=1WOEtVnxqYdHl35pAyIcp-H0HtTjI-l3V
./demo.py logs/R-50-C4_x1_caffe2_to_chainer --img https://raw.githubusercontent.com/facebookresearch/Detectron/master/demo/33823288584_1d21cf0a26_k.jpg
./demo.py logs/R-50-C4_x1_caffe2_to_chainer --img https://raw.githubusercontent.com/facebookresearch/Detectron/master/demo/17790319373_bd19b24cfc_k.jpg
Fig 2. Mask R-CNN, ResNet50, 8GPU, Copied from Detectron, COCO 31.4 mAP@50:95
Installation & Training
Single GPU Training
# Install Chainer Mask R-CNN.
pip install opencv-python
pip install .
# Run training!
cd examples/coco && ./train.py --gpu 0
Multi GPU Training
# Install OpenMPI
wget https://www.open-mpi.org/software/ompi/v3.0/downloads/openmpi-3.0.0.tar.gz
tar zxvf openmpi-3.0.0.tar.gz
cd openmpi-3.0.0
./configure --with-cuda
make -j4
sudo make install
sudo ldconfig
# Install NCCL
# dpkg -i nccl-repo-ubuntu1404-2.1.4-ga-cuda8.0_1-1_amd64.deb
dpkg -i nccl-repo-ubuntu1604-2.1.15-ga-cuda9.1_1-1_amd64.deb
sudo apt update
sudo apt install libnccl2 libnccl-dev
# Install ChainerMN
pip install chainermn
# Finally, install Chainer Mask R-CNN.
pip install opencv-python
pip install .
# Run training!
cd examples/coco && mpirun -n 4 ./train.py --multi-node
Testing
pip install flake8 pytest
flake8 .
pytest -v tests
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