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‎.github/ISSUE_TEMPLATE/error-report.md

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---
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name: Error report
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about: Create a report to help us improve
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title: ''
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labels: ''
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assignees: ''
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---
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Thanks for your error report and we appreciate it a lot.
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**Checklist**
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1. I have searched related issues but cannot get the expected help.
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2. The bug has not been fixed in the latest version.
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**Describe the bug**
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A clear and concise description of what the bug is.
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**Reproduction**
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1. What command or script did you run?
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```
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A placeholder for the command.
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```
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2. Did you make any modifications on the code or config? Did you understand what you have modified?
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3. What dataset did you use?
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**Environment**
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- OS: [e.g., Ubuntu 16.04.6]
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- GCC [e.g., 5.4.0]
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- PyTorch version [e.g., 1.1.0]
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- How you installed PyTorch [e.g., pip, conda, source]
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- GPU model [e.g., 1080Ti, V100]
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- CUDA and CUDNN version
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- [optional] Other information that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.)
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**Error traceback**
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If applicable, paste the error trackback here.
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```
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A placeholder for trackback.
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```
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**Bug fix**
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If you have already identified the reason, you can provide the information here. If you are willing to create a PR to fix it, please also leave a comment here and that would be much appreciated!

‎.github/ISSUE_TEMPLATE/feature_request.md

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---
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name: Feature request
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about: Suggest an idea for this project
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title: ''
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labels: ''
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assignees: ''
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---
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**Describe the feature**
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**Motivation**
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A clear and concise description of the motivation of the feature.
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Ex1. It is inconvenient when [....].
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Ex2. There is a recent paper [....], which is very helpful for [....].
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**Related resources**
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If there is an official code release or third-party implementations, please also provide the information here, which would be very helpful.
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**Additional context**
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Add any other context or screenshots about the feature request here.
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If you would like to implement the feature and create a PR, please leave a comment here and that would be much appreciated.

‎.github/ISSUE_TEMPLATE/general_questions.md

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---
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name: General questions
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about: Ask general questions to get help
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title: ''
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labels: ''
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assignees: ''
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---
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‎.gitignore

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# pyenv
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.python-version
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# celery beat schedule file
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celerybeat-schedule
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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# cython generated cpp
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mmdet/ops/nms/src/soft_nms_cpu.cpp
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mmdet/version.py
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data
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.vscode
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.idea
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# custom
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*.pkl
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*.pkl.json
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*.log.json
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work_dirs/
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# Pytorch
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*.pth

‎.isort.cfg

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[isort]
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line_length = 79
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multi_line_output = 0
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known_first_party = mmdet
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known_third_party = mmcv,numpy,matplotlib,pycocotools,six,seaborn,terminaltables,torch,torchvision
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no_lines_before = STDLIB,LOCALFOLDER
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default_section = THIRDPARTY

‎.style.yapf

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[style]
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BASED_ON_STYLE = pep8
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BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true
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SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true

‎.travis.yml

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dist: xenial
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language: python
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install:
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- pip install isort flake8 yapf
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python:
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- "3.5"
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- "3.6"
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- "3.7"
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script:
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- flake8
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- isort -rc --check-only --diff mmdet/ tools/
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- yapf -r -d --style .style.yapf mmdet/ tools/

‎INSTALL.md

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## Installation
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### Requirements
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- Linux (Windows is not officially supported)
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- Python 3.5+ (Python 2 is not supported)
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- PyTorch 1.1 or higher
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- CUDA 9.0 or higher
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- NCCL 2
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- GCC 4.9 or higher
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- [mmcv](https://github.com/open-mmlab/mmcv) with version number 0.2.13 or 0.2.14
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We have tested the following versions of OS and CUDA:
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- OS: Ubuntu 16.04/18.04
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- CUDA: 10.0
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### Install mmdetection
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a. Create a conda virtual environment and activate it.
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```shell
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conda create -n ppdet python=3.7 -y
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conda activate ppdet
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```
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b. Install PyTorch stable or nightly and torchvision following the [official instructions](https://pytorch.org/), e.g.,
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```shell
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conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=10.0 -c pytorch
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```
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c. Clone the PPDet repository.
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```shell
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git clone https://github.com/nerminsamet/ppdet.git
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cd ppdet
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```
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d. Install mmcv
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```shell
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pip install mmcv==0.2.13
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```
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e. Install PPDet (other dependencies will be installed automatically).
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```shell
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python setup.py develop
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# or "pip install -v -e ."
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```
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Note:
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1. The git commit id will be written to the version number with step e, e.g. 1.0.rc. The version will also be saved in trained models.
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It is recommended that you run step e each time you pull some updates from github. **If C/CUDA codes are modified, then step e is compulsory.**
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2. Following the above instructions, PPDet is installed on `dev` mode, **any local modifications made to the code will take effect without the need to reinstall it** (unless you submit some commits and want to update the version number).
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### Prepare datasets
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It is recommended to symlink the dataset root to `$PPDet/data`.
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If your folder structure is different, you may need to change the corresponding paths in config files.
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```
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COCO_DIR
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├── annotations
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├── images
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│ ├── train2017
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│ ├── val2017
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│ ├── test2017
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```
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### Scripts
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[Here](https://gist.github.com/hellock/bf23cd7348c727d69d48682cb6909047) is
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a script for setting up mmdetection with conda.
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### Multiple versions
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If there are more than one mmdetection on your machine, and you want to use them alternatively, the recommended way is to create multiple conda environments and use different environments for different versions.
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Another way is to insert the following code to the main scripts (`train.py`, `test.py` or any other scripts you run)
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```python
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import os.path as osp
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import sys
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sys.path.insert(0, osp.join(osp.dirname(osp.abspath(__file__)), '../'))
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```
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or run the following command in the terminal of corresponding folder.
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```shell
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export PYTHONPATH=`pwd`:$PYTHONPATH
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```

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