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instance-segmenation-with-maskrcnn

Use MaskRCNN to do the Instance segmentation

https://github.com/skchen1993/instance-segmenation-with-maskrcnn

Science Score: 44.0%

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Repository

Use MaskRCNN to do the Instance segmentation

Basic Info
  • Host: GitHub
  • Owner: skchen1993
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 25.7 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 4 years ago · Last pushed about 4 years ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

Using MaskRCNN for nuclei instance segmentation

Roport is here!!

MaskRCNN for nuclei instance segmentation

  • nuclei dataset: 24 training image (each with hundreds of mask label), 6 testing image

Environment

  • Python 3.7.3
  • Pytorch 1.7.0
  • CUDA 10.2

step for producing submission

  1. Install requirement
  2. Download pretrained model and annotaion files
  3. Model config setting
  4. Training
  5. Testing

Install requirement

  • Download project:

  • Install mmdetection:
    Follow the official setup instruction in mmdetection github (link)

  • Download annotayion files:
    Annotation file
    Download the annotation files and put them into ./data/coco/annotations/
    (You can use jupyter notebook file here to visualize you annotation file on image to make sure your annotation file is correct)

  • Download model weight:
    checkpoints
    Download the model weight and put them into ./checkpoints

Model config setting

check the model config file in ./configs/mask_rcnn/nuclei.py
modified the work_dir, load_from, ann_file

Training

  • Training the model:
    First, download the ResNext101 pretrained model(here) weight and then put it into ./checkpoints/ , and remember to modified the load_from path in model config.

  • use command below to train:
    ./tools/dist_train.sh ./configs/mask_rcnn/nuclei.py {number of GPU}

Testing

  • Prepare the trained model weight and put it into ./checkpoints/, and remember to modified the load_from path in model config.

  • use command below to test and generate json.file:
    python tools/test.py ./configs/mask_rcnn/nuclei.py {path of trained model} --format-only --options "jsonfile_prefix={path of result}"

  • if you want to visualize testing result, cau use command below:
    python tools/test.py ./configs/mask_rcnn/nuclei.py {path of trained model} --eval bbox segm --show

Reference

mmdetection

Owner

  • Login: skchen1993
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - name: "MMDetection Contributors"
title: "OpenMMLab Detection Toolbox and Benchmark"
date-released: 2018-08-22
url: "https://github.com/open-mmlab/mmdetection"
license: Apache-2.0

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Dependencies

.github/workflows/build.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
  • codecov/codecov-action v1.0.10 composite
  • codecov/codecov-action v2 composite
.github/workflows/build_pat.yml actions
  • actions/checkout v2 composite
.github/workflows/deploy.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
.github/workflows/lint.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
docker/serve/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
requirements/build.txt pypi
  • cython *
  • numpy *
requirements/docs.txt pypi
  • docutils ==0.16.0
  • recommonmark *
  • sphinx ==4.0.2
  • sphinx-copybutton *
  • sphinx_markdown_tables *
  • sphinx_rtd_theme ==0.5.2
requirements/mminstall.txt pypi
  • mmcv-full >=1.3.17
requirements/optional.txt pypi
  • cityscapesscripts *
  • imagecorruptions *
  • scipy *
  • sklearn *
requirements/readthedocs.txt pypi
  • mmcv *
  • torch *
  • torchvision *
requirements/runtime.txt pypi
  • matplotlib *
  • numpy *
  • pycocotools *
  • pycocotools-windows *
  • six *
  • terminaltables *
requirements/tests.txt pypi
  • asynctest * test
  • codecov * test
  • flake8 * test
  • interrogate * test
  • isort ==4.3.21 test
  • kwarray * test
  • onnx ==1.7.0 test
  • onnxruntime >=1.8.0 test
  • pytest * test
  • ubelt * test
  • xdoctest >=0.10.0 test
  • yapf * test