Metadata-Version: 2.1
Name: chainer-mask-rcnn
Version: 0.5.19
Summary: Chainer Implementation of Mask R-CNN.
Home-page: http://github.com/wkentaro/chainer-mask-rcnn
Author: Kentaro Wada
Author-email: www.kentaro.wada@gmail.com
License: MIT
Description: # chainer-mask-rcnn
        
        [![PyPI version](https://badge.fury.io/py/chainer-mask-rcnn.svg)](https://badge.fury.io/py/chainer-mask-rcnn)
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        Chainer Implementation of [Mask R-CNN](https://arxiv.org/abs/1703.06870).
        
        ## Features
        
        - [x] ResNet50, ResNet101 backbone.
        - [x] [VOC and COCO training examples](https://github.com/wkentaro/chainer-mask-rcnn/blob/master/examples).
        - [x] **[Reproduced result of original work (ResNet50, COCO)](https://github.com/wkentaro/chainer-mask-rcnn/blob/master/#coco-results)**.
        - [x] Weight copy from pretrained model at [facebookresearch/Detectron](https://github.com/facebookresearch/Detectron).
        - [x] Training with batch size >= 2.
        - [ ] Support FPN backbones.
        - [ ] Keypoint detection.
        
        <img src="https://github.com/wkentaro/chainer-mask-rcnn/blob/master/examples/coco/.readme/R-50-C4_x1_33823288584_1d21cf0a26_k.jpg?raw=true" width="44.3%" /> <img src="https://github.com/wkentaro/chainer-mask-rcnn/blob/master/examples/coco/.readme/R-50-C4_x1_17790319373_bd19b24cfc_k.jpg?raw=true" width="52%" />  
        *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](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) |
        | 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) |
        | FCIS, ResNet50 | [FCIS](https://github.com/msracver/FCIS) | 8 | 27.1 | - |
        
        
        ## Inference
        
        ```bash
        # 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
        ```
        
        <img src="https://github.com/wkentaro/chainer-mask-rcnn/blob/master/examples/coco/.readme/R-50-C4_x1_caffe2_to_chainer_result_33823288584_1d21cf0a26_k.jpg?raw=true" width="44.3%" /> <img src="https://github.com/wkentaro/chainer-mask-rcnn/blob/master/examples/coco/.readme/R-50-C4_x1_caffe2_to_chainer_result_17790319373_bd19b24cfc_k.jpg?raw=true" width="52%" />  
        *Fig 2. Mask R-CNN, ResNet50, 8GPU, Copied from Detectron, COCO 31.4 mAP@50:95*
        
        
        ## Installation & Training
        
        
        ### Single GPU Training
        
        ```bash
        # Install Chainer Mask R-CNN.
        pip install opencv-python
        pip install .
        
        # Run training!
        cd examples/coco && ./train.py --gpu 0
        ```
        
        
        ### Multi GPU Training
        
        ```bash
        # 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
        
        ```bash
        pip install flake8 pytest
        flake8 .
        pytest -v tests
        ```
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Description-Content-Type: text/markdown
