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mmdetection_mask_r-cnn

https://github.com/spongebob-0715/mmdetection_mask_r-cnn

Science Score: 44.0%

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    CITATION.cff file
    Found CITATION.cff file
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    codemeta.json file
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    .zenodo.json file
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    Scientific vocabulary similarity
    Low similarity (3.2%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Basic Info
  • Host: GitHub
  • Owner: SpongeBob-0715
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Size: 13 MB
Statistics
  • Stars: 0
  • Watchers: 0
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created 9 months ago · Last pushed 9 months ago
Metadata Files
Readme Contributing License Code of conduct Citation

README.md

MMDetection 环境搭建指南

本项目旨在指导用户使用 Conda 和 OpenMMLab 工具链快速搭建 MMDetection 环境。

环境配置步骤

步骤 1：创建并激活 Conda 虚拟环境

bash conda create --name openmmlab python=3.8 -y conda activate openmmlab

步骤 2：安装 PyTorch（基于官方说明）

如果使用 GPU 平台：

bash conda install pytorch torchvision -c pytorch

⚠️ 请根据你的实际设备和 CUDA 版本选择合适的 PyTorch 安装方式。可参考 PyTorch 官方安装指南。

步骤 3：使用 MIM 安装 MMEngine 和 MMCV

bash pip install -U openmim mim install mmengine mim install "mmcv>=2.0.0"

步骤 4：安装 MMDetection

bash git clone https://github.com/open-mmlab/mmdetection.git cd mmdetection pip install -v -e .

说明：

  • -v：启用详细输出，方便调试安装过程。
  • -e：以可编辑模式安装，支持本地代码修改后无需重新安装。

模型训练

训练 Mask R-CNN 模型

bash cd mmdetection bash run_train_mask_rcnn.sh

训练 Sparse R-CNN 模型

bash cd mmdetection bash run_train_sparse_rcnn.sh

测试模型

共有三个代码，在 eval文件夹中，只需填入模型路径，即可进行测试

参考链接

Owner

  • Name: Tingyun Li
  • Login: SpongeBob-0715
  • Kind: user
  • Company: Independent Researcher

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/deploy.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
.circleci/docker/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
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
docker/serve_cn/Dockerfile docker
  • pytorch/pytorch ${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel build
requirements/albu.txt pypi
  • albumentations >=0.3.2
requirements/build.txt pypi
  • cython *
  • numpy *
requirements/docs.txt pypi
  • docutils ==0.16.0
  • myst-parser *
  • sphinx ==4.0.2
  • sphinx-copybutton *
  • sphinx_markdown_tables *
  • sphinx_rtd_theme ==0.5.2
  • urllib3 <2.0.0
requirements/mminstall.txt pypi
  • mmcv >=2.0.0rc4,<2.2.0
  • mmengine >=0.7.1,<1.0.0
requirements/multimodal.txt pypi
  • fairscale *
  • jsonlines *
  • nltk *
  • pycocoevalcap *
  • transformers *
requirements/optional.txt pypi
  • cityscapesscripts *
  • emoji *
  • fairscale *
  • imagecorruptions *
  • scikit-learn *
requirements/readthedocs.txt pypi
  • mmcv >=2.0.0rc4,<2.2.0
  • mmengine >=0.7.1,<1.0.0
  • scipy *
  • torch *
  • torchvision *
  • urllib3 <2.0.0
requirements/runtime.txt pypi
  • matplotlib *
  • numpy *
  • pycocotools *
  • scipy *
  • shapely *
  • six *
  • terminaltables *
  • tqdm *
requirements/tests.txt pypi
  • asynctest * test
  • cityscapesscripts * test
  • codecov * test
  • flake8 * test
  • imagecorruptions * test
  • instaboostfast * test
  • interrogate * test
  • isort ==4.3.21 test
  • kwarray * test
  • memory_profiler * test
  • nltk * test
  • onnx ==1.7.0 test
  • onnxruntime >=1.8.0 test
  • parameterized * test
  • prettytable * test
  • protobuf <=3.20.1 test
  • psutil * test
  • pytest * test
  • transformers * test
  • ubelt * test
  • xdoctest >=0.10.0 test
  • yapf * test
requirements/tracking.txt pypi
  • mmpretrain *
  • motmetrics *
  • numpy <1.24.0
  • scikit-learn *
  • seaborn *
requirements.txt pypi
setup.py pypi