{ "info": { "author": "Kentaro Wada", "author_email": "www.kentaro.wada@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Natural Language :: English", "Programming Language :: Python", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: Implementation :: CPython" ], "description": "# chainer-mask-rcnn\n\n[![PyPI version](https://badge.fury.io/py/chainer-mask-rcnn.svg)](https://badge.fury.io/py/chainer-mask-rcnn)\n[![Python Versions](https://img.shields.io/pypi/pyversions/chainer-mask-rcnn.svg)](https://pypi.org/project/chainer-mask-rcnn)\n[![GitHub Actions](https://github.com/wkentaro/chainer-mask-rcnn/workflows/CI/badge.svg)](https://github.com/wkentaro/chainer-mask-rcnn/actions)\n\nChainer Implementation of [Mask R-CNN](https://arxiv.org/abs/1703.06870).\n\n## Features\n\n- [x] ResNet50, ResNet101 backbone.\n- [x] [VOC and COCO training examples](https://github.com/wkentaro/chainer-mask-rcnn/blob/master/examples).\n- [x] **[Reproduced result of original work (ResNet50, COCO)](https://github.com/wkentaro/chainer-mask-rcnn/blob/master/#coco-results)**.\n- [x] Weight copy from pretrained model at [facebookresearch/Detectron](https://github.com/facebookresearch/Detectron).\n- [x] Training with batch size >= 2.\n- [ ] Support FPN backbones.\n- [ ] Keypoint detection.\n\n \n*Fig 1. Mask R-CNN, ResNet50, 8GPU, Ours, COCO 31.4 mAP@50:95*\n\n\n\n## COCO Results\n\n| Model | Implementation | N gpu training | mAP@50:95 | Log |\n|-------|----------------|----------------|-----------|-----|\n| Mask R-CNN, ResNet50 | [Ours](https://github.com/wkentaro/chainer-mask-rcnn/blob/master/.?raw=true) | 8 | 31.5 - 31.8 | [Log](https://drive.google.com/open?id=1WOEtVnxqYdHl35pAyIcp-H0HtTjI-l3V) |\n| Mask R-CNN, ResNet50 | [Detectron](https://github.com/facebookresearch/Detectron) | 8 | 31.4 (30.8 after copied) | [Log](https://drive.google.com/open?id=1xQBox3uMv2FoyXXpsC9ASNZ-92NgAbcT) |\n| FCIS, ResNet50 | [FCIS](https://github.com/msracver/FCIS) | 8 | 27.1 | - |\n\n\n## Inference\n\n```bash\n# you can use your trained model\n./demo.py logs/ --img \n\n# COCO Example: Mask R-CNN, ResNet50, 31.4 mAP@50:95\ncd examples/coco\nLOG_DIR=logs/20180730_081433\nmkdir -p $LOG_DIR\npip install gdown\ngdown https://drive.google.com/uc?id=1XC-Mx4HX0YBIy0Fbp59EjJFOF7a3XK0R -O $LOG_DIR/snapshot_model.npz\ngdown https://drive.google.com/uc?id=1fXHanL2pBakbkv83wn69QhI6nM6KjrzL -O $LOG_DIR/params.yaml\n./demo.py $LOG_DIR\n\n# copy weight from caffe2 to chainer\ncd examples/coco\n./convert_caffe2_to_chainer.py # or download from https://drive.google.com/open?id=1WOEtVnxqYdHl35pAyIcp-H0HtTjI-l3V\n./demo.py logs/R-50-C4_x1_caffe2_to_chainer --img https://raw.githubusercontent.com/facebookresearch/Detectron/master/demo/33823288584_1d21cf0a26_k.jpg\n./demo.py logs/R-50-C4_x1_caffe2_to_chainer --img https://raw.githubusercontent.com/facebookresearch/Detectron/master/demo/17790319373_bd19b24cfc_k.jpg\n```\n\n \n*Fig 2. Mask R-CNN, ResNet50, 8GPU, Copied from Detectron, COCO 31.4 mAP@50:95*\n\n\n## Installation & Training\n\n\n### Single GPU Training\n\n```bash\n# Install Chainer Mask R-CNN.\npip install opencv-python\npip install .\n\n# Run training!\ncd examples/coco && ./train.py --gpu 0\n```\n\n\n### Multi GPU Training\n\n```bash\n# Install OpenMPI\nwget https://www.open-mpi.org/software/ompi/v3.0/downloads/openmpi-3.0.0.tar.gz\ntar zxvf openmpi-3.0.0.tar.gz\ncd openmpi-3.0.0\n./configure --with-cuda\nmake -j4\nsudo make install\nsudo ldconfig\n\n# Install NCCL\n# dpkg -i nccl-repo-ubuntu1404-2.1.4-ga-cuda8.0_1-1_amd64.deb\ndpkg -i nccl-repo-ubuntu1604-2.1.15-ga-cuda9.1_1-1_amd64.deb\nsudo apt update\nsudo apt install libnccl2 libnccl-dev\n\n# Install ChainerMN\npip install chainermn\n\n# Finally, install Chainer Mask R-CNN.\npip install opencv-python\npip install .\n\n# Run training!\ncd examples/coco && mpirun -n 4 ./train.py --multi-node\n```\n\n\n## Testing\n\n```bash\npip install flake8 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