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Add custom maskrcnn-benchmark
- support training when no ground truth boxes are present - support RandomGrayscale data augmentation
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‎.dockerignore‎

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*
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!maskrcnn_benchmark/engine/trainer.py
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!maskrcnn_benchmark/modeling/matcher.py
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!maskrcnn_benchmark/modeling/roi_heads/box_head/loss.py
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!maskrcnn_benchmark/modeling/rpn/loss.py
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!maskrcnn_benchmark/config/defaults.py
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!maskrcnn_benchmark/data/transforms/__init__.py
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!maskrcnn_benchmark/data/transforms/build.py
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!maskrcnn_benchmark/data/transforms/transforms.py
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!maskrcnn_benchmark/engine/trainer.py
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!maskrcnn_benchmark/engine/bbox_aug.py
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!maskrcnn_benchmark/config/paths_catalog.py
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!maskrcnn_benchmark/data/datasets/__init__.py
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!maskrcnn_benchmark/data/datasets/kuzushiji.py
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!maskrcnn_benchmark/data/datasets/evaluation/__init__.py
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!maskrcnn_benchmark/data/datasets/evaluation/kuzushiji/__init__.py
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!maskrcnn_benchmark/data/datasets/evaluation/kuzushiji/kuzushiji_eval.py
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!requirements.txt

‎Dockerfile‎

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ARG CUDA="10.0"
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ARG CUDNN="7"
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FROM nvidia/cuda:${CUDA}-cudnn${CUDNN}-devel-ubuntu16.04
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RUN echo 'debconf debconf/frontend select Noninteractive' | debconf-set-selections
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# install basics
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RUN apt-get update -y \
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&& apt-get install -y apt-utils git curl ca-certificates bzip2 cmake tree htop bmon iotop g++ \
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&& apt-get install -y libglib2.0-0 libsm6 libxext6 libxrender-dev
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# Install Miniconda
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RUN curl -so /miniconda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \
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&& chmod +x /miniconda.sh \
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&& /miniconda.sh -b -p /miniconda \
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&& rm /miniconda.sh
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ENV PATH=/miniconda/bin:$PATH
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# Create a Python 3.6 environment
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RUN /miniconda/bin/conda install -y conda-build \
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&& /miniconda/bin/conda create -y --name py36 python=3.6.7 \
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&& /miniconda/bin/conda clean -ya
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ENV CONDA_DEFAULT_ENV=py36
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ENV CONDA_PREFIX=/miniconda/envs/$CONDA_DEFAULT_ENV
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ENV PATH=$CONDA_PREFIX/bin:$PATH
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ENV CONDA_AUTO_UPDATE_CONDA=false
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RUN conda install -y ipython
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RUN pip install requests ninja yacs cython matplotlib opencv-python tqdm
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# Install PyTorch
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ARG CUDA
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RUN conda install pytorch=1.2 torchvision cudatoolkit=${CUDA} -c pytorch \
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&& conda clean -ya
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# install pycocotools
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RUN git clone https://github.com/cocodataset/cocoapi.git \
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&& cd cocoapi/PythonAPI \
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&& python setup.py build_ext install
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# install apex
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RUN git clone https://github.com/NVIDIA/apex.git \
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&& cd apex \
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&& python setup.py install --cuda_ext --cpp_ext
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RUN apt-get install -y vim
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# install PyTorch Detection
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ARG FORCE_CUDA="1"
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ENV FORCE_CUDA=${FORCE_CUDA}
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RUN git clone https://github.com/facebookresearch/maskrcnn-benchmark.git /maskrcnn-benchmark
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WORKDIR /maskrcnn-benchmark
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COPY ./maskrcnn_benchmark/engine/trainer.py maskrcnn_benchmark/engine/
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COPY ./maskrcnn_benchmark/modeling/matcher.py maskrcnn_benchmark/modeling/
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COPY ./maskrcnn_benchmark/modeling/roi_heads/box_head/loss.py maskrcnn_benchmark/modeling/roi_heads/box_head/
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COPY ./maskrcnn_benchmark/modeling/rpn/loss.py maskrcnn_benchmark/modeling/rpn/
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COPY ./maskrcnn_benchmark/config/defaults.py maskrcnn_benchmark/config/
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COPY ./maskrcnn_benchmark/data/transforms/__init__.py maskrcnn_benchmark/data/transforms/
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COPY ./maskrcnn_benchmark/data/transforms/build.py maskrcnn_benchmark/data/transforms/
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COPY ./maskrcnn_benchmark/data/transforms/transforms.py maskrcnn_benchmark/data/transforms/
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COPY ./maskrcnn_benchmark/engine/trainer.py maskrcnn_benchmark/engine/
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COPY ./maskrcnn_benchmark/engine/bbox_aug.py maskrcnn_benchmark/engine/
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COPY ./maskrcnn_benchmark/config/paths_catalog.py maskrcnn_benchmark/config/
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COPY ./maskrcnn_benchmark/data/datasets/__init__.py maskrcnn_benchmark/data/datasets/
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COPY ./maskrcnn_benchmark/data/datasets/kuzushiji.py maskrcnn_benchmark/data/datasets/
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COPY ./maskrcnn_benchmark/data/datasets/evaluation/__init__.py maskrcnn_benchmark/data/datasets/evaluation/
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COPY ./maskrcnn_benchmark/data/datasets/evaluation/kuzushiji/__init__.py maskrcnn_benchmark/data/datasets/evaluation/kuzushiji/
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COPY ./maskrcnn_benchmark/data/datasets/evaluation/kuzushiji/kuzushiji_eval.py maskrcnn_benchmark/data/datasets/evaluation/kuzushiji/
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RUN cd /maskrcnn-benchmark \
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&& python setup.py build develop
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RUN pip install pandas
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COPY ./requirements.txt .
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RUN pip install -r requirements.txt
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WORKDIR /work

‎build.sh‎

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docker build -t knjcode/kuzushiji-recognition-docker --build-arg CUDA=10.0 --build-arg CUDNN=7 .

‎configs/caffe2/e2e_faster_rcnn_R_101_FPN_1x_caffe2.yaml‎

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MODEL:
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META_ARCHITECTURE: "GeneralizedRCNN"
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WEIGHT: "catalog://Caffe2Detectron/COCO/35857890/e2e_faster_rcnn_R-101-FPN_1x"
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BACKBONE:
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CONV_BODY: "R-101-FPN"
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RESNETS:
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BACKBONE_OUT_CHANNELS: 256
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RPN:
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USE_FPN: True
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ANCHOR_STRIDE: (4, 8, 16, 32, 64)
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PRE_NMS_TOP_N_TRAIN: 2000
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PRE_NMS_TOP_N_TEST: 1000
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POST_NMS_TOP_N_TEST: 1000
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FPN_POST_NMS_TOP_N_TEST: 1000
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ROI_HEADS:
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USE_FPN: True
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ROI_BOX_HEAD:
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POOLER_RESOLUTION: 7
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POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
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POOLER_SAMPLING_RATIO: 2
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FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
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PREDICTOR: "FPNPredictor"
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DATASETS:
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TEST: ("coco_2014_minival",)
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DATALOADER:
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SIZE_DIVISIBILITY: 32

‎configs/caffe2/e2e_faster_rcnn_R_50_C4_1x_caffe2.yaml‎

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MODEL:
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META_ARCHITECTURE: "GeneralizedRCNN"
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WEIGHT: "catalog://Caffe2Detectron/COCO/35857197/e2e_faster_rcnn_R-50-C4_1x"
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DATASETS:
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TEST: ("coco_2014_minival",)

‎configs/caffe2/e2e_faster_rcnn_R_50_FPN_1x_caffe2.yaml‎

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MODEL:
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META_ARCHITECTURE: "GeneralizedRCNN"
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WEIGHT: "catalog://Caffe2Detectron/COCO/35857345/e2e_faster_rcnn_R-50-FPN_1x"
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BACKBONE:
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CONV_BODY: "R-50-FPN"
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RESNETS:
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BACKBONE_OUT_CHANNELS: 256
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RPN:
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USE_FPN: True
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ANCHOR_STRIDE: (4, 8, 16, 32, 64)
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PRE_NMS_TOP_N_TRAIN: 2000
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PRE_NMS_TOP_N_TEST: 1000
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POST_NMS_TOP_N_TEST: 1000
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FPN_POST_NMS_TOP_N_TEST: 1000
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ROI_HEADS:
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USE_FPN: True
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ROI_BOX_HEAD:
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POOLER_RESOLUTION: 7
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POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
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POOLER_SAMPLING_RATIO: 2
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FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
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PREDICTOR: "FPNPredictor"
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DATASETS:
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TEST: ("coco_2014_minival",)
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DATALOADER:
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SIZE_DIVISIBILITY: 32

‎configs/caffe2/e2e_faster_rcnn_X_101_32x8d_FPN_1x_caffe2.yaml‎

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MODEL:
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META_ARCHITECTURE: "GeneralizedRCNN"
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WEIGHT: "catalog://Caffe2Detectron/COCO/36761737/e2e_faster_rcnn_X-101-32x8d-FPN_1x"
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BACKBONE:
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CONV_BODY: "R-101-FPN"
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RESNETS:
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BACKBONE_OUT_CHANNELS: 256
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STRIDE_IN_1X1: False
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NUM_GROUPS: 32
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WIDTH_PER_GROUP: 8
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RPN:
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USE_FPN: True
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ANCHOR_STRIDE: (4, 8, 16, 32, 64)
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PRE_NMS_TOP_N_TRAIN: 2000
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PRE_NMS_TOP_N_TEST: 1000
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POST_NMS_TOP_N_TEST: 1000
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FPN_POST_NMS_TOP_N_TEST: 1000
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ROI_HEADS:
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USE_FPN: True
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ROI_BOX_HEAD:
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POOLER_RESOLUTION: 7
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POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
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POOLER_SAMPLING_RATIO: 2
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FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
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PREDICTOR: "FPNPredictor"
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DATASETS:
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TEST: ("coco_2014_minival",)
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DATALOADER:
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SIZE_DIVISIBILITY: 32

‎configs/caffe2/e2e_keypoint_rcnn_R_50_FPN_1x_caffe2.yaml‎

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MODEL:
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META_ARCHITECTURE: "GeneralizedRCNN"
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WEIGHT: "catalog://Caffe2Detectron/COCO/37697547/e2e_keypoint_rcnn_R-50-FPN_1x"
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BACKBONE:
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CONV_BODY: "R-50-FPN"
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RESNETS:
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BACKBONE_OUT_CHANNELS: 256
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RPN:
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USE_FPN: True
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ANCHOR_STRIDE: (4, 8, 16, 32, 64)
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PRE_NMS_TOP_N_TRAIN: 2000
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PRE_NMS_TOP_N_TEST: 1000
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POST_NMS_TOP_N_TEST: 1000
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FPN_POST_NMS_TOP_N_TEST: 1000
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ROI_HEADS:
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USE_FPN: True
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ROI_BOX_HEAD:
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POOLER_RESOLUTION: 7
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POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
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POOLER_SAMPLING_RATIO: 2
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FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
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PREDICTOR: "FPNPredictor"
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NUM_CLASSES: 2
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ROI_KEYPOINT_HEAD:
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POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
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FEATURE_EXTRACTOR: "KeypointRCNNFeatureExtractor"
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PREDICTOR: "KeypointRCNNPredictor"
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POOLER_RESOLUTION: 14
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POOLER_SAMPLING_RATIO: 2
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RESOLUTION: 56
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SHARE_BOX_FEATURE_EXTRACTOR: False
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KEYPOINT_ON: True
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DATASETS:
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TRAIN: ("keypoints_coco_2014_train", "keypoints_coco_2014_valminusminival",)
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TEST: ("keypoints_coco_2014_minival",)
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INPUT:
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MIN_SIZE_TRAIN: (640, 672, 704, 736, 768, 800)
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DATALOADER:
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SIZE_DIVISIBILITY: 32
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SOLVER:
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BASE_LR: 0.02
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WEIGHT_DECAY: 0.0001
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STEPS: (60000, 80000)
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MAX_ITER: 90000

‎configs/caffe2/e2e_mask_rcnn_R_101_FPN_1x_caffe2.yaml‎

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MODEL:
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META_ARCHITECTURE: "GeneralizedRCNN"
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WEIGHT: "catalog://Caffe2Detectron/COCO/35861795/e2e_mask_rcnn_R-101-FPN_1x"
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BACKBONE:
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CONV_BODY: "R-101-FPN"
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RESNETS:
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BACKBONE_OUT_CHANNELS: 256
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RPN:
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USE_FPN: True
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ANCHOR_STRIDE: (4, 8, 16, 32, 64)
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PRE_NMS_TOP_N_TRAIN: 2000
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PRE_NMS_TOP_N_TEST: 1000
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POST_NMS_TOP_N_TEST: 1000
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FPN_POST_NMS_TOP_N_TEST: 1000
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ROI_HEADS:
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USE_FPN: True
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ROI_BOX_HEAD:
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POOLER_RESOLUTION: 7
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POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
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POOLER_SAMPLING_RATIO: 2
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FEATURE_EXTRACTOR: "FPN2MLPFeatureExtractor"
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PREDICTOR: "FPNPredictor"
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ROI_MASK_HEAD:
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POOLER_SCALES: (0.25, 0.125, 0.0625, 0.03125)
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FEATURE_EXTRACTOR: "MaskRCNNFPNFeatureExtractor"
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PREDICTOR: "MaskRCNNC4Predictor"
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POOLER_RESOLUTION: 14
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POOLER_SAMPLING_RATIO: 2
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RESOLUTION: 28
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SHARE_BOX_FEATURE_EXTRACTOR: False
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MASK_ON: True
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DATASETS:
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TEST: ("coco_2014_minival",)
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DATALOADER:
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SIZE_DIVISIBILITY: 32

‎configs/caffe2/e2e_mask_rcnn_R_50_C4_1x_caffe2.yaml‎

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MODEL:
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META_ARCHITECTURE: "GeneralizedRCNN"
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WEIGHT: "catalog://Caffe2Detectron/COCO/35858791/e2e_mask_rcnn_R-50-C4_1x"
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ROI_MASK_HEAD:
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PREDICTOR: "MaskRCNNC4Predictor"
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SHARE_BOX_FEATURE_EXTRACTOR: True
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MASK_ON: True
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DATASETS:
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TEST: ("coco_2014_minival",)

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