资源简介
mask rcnn训练自己数据,下载更换数据集和路径即可使用
mask rcnn训练自己数据,下载更换数据集和路径即可使用
mask rcnn训练自己数据,下载更换数据集和路径即可使用
代码片段和文件信息
“““
The build/compilations setup
>> pip install -r requirements.txt
>> python setup.py install
“““
import pip
import logging
import pkg_resources
try:
from setuptools import setup
except ImportError:
from distutils.core import setup
def _parse_requirements(file_path):
pip_ver = pkg_resources.get_distribution(‘pip‘).version
pip_version = list(map(int pip_ver.split(‘.‘)[:2]))
if pip_version >= [6 0]:
raw = pip.req.parse_requirements(file_path
session=pip.download.PipSession())
else:
raw = pip.req.parse_requirements(file_path)
return [str(i.req) for i in raw]
# parse_requirements() returns generator of pip.req.InstallRequirement objects
try:
install_reqs = _parse_requirements(“requirements.txt“)
except Exception:
logging.warning(‘Fail load requirements file so using default ones.‘)
install_reqs = []
setup(
name=‘mask-rcnn‘
version=‘2.1‘
url=‘https://github.com/matterport/Mask_RCNN‘
author=‘Matterport‘
author_email=‘waleed.abdulla@gmail.com‘
license=‘MIT‘
description=‘Mask R-CNN for object detection and instance segmentation‘
packages=[“mrcnn“]
install_requires=install_reqs
include_package_data=True
python_requires=‘>=3.4‘
long_description=“““This is an implementation of Mask R-CNN on Python 3 Keras and TensorFlow.
The model generates bounding boxes and segmentation masks for each instance of an object in the image.
It‘s based on Feature Pyramid Network (FPN) and a ResNet101 backbone.“““
classifiers=[
“Development Status :: 5 - Production/Stable“
“Environment :: Console“
“Intended Audience :: Developers“
“Intended Audience :: Information Technology“
“Intended Audience :: Education“
“Intended Audience :: Science/Research“
“License :: OSI Approved :: MIT License“
“Natural Language :: English“
“Operating System :: OS Independent“
“Topic :: Scientific/Engineering :: Artificial Intelligence“
“Topic :: Scientific/Engineering :: Image Recognition“
“Topic :: Scientific/Engineering :: Visualization“
“Topic :: Scientific/Engineering :: Image Segmentation“
‘Programming Language :: Python :: 3.4‘
‘Programming Language :: Python :: 3.5‘
‘Programming Language :: Python :: 3.6‘
]
keywords=“image instance segmentation object detection mask rcnn r-cnn tensorflow keras“
)
属性 大小 日期 时间 名称
----------- --------- ---------- ----- ----
文件 569 2018-06-06 14:12 .gitignore
目录 0 2019-04-25 19:52 .spyproject\
文件 62 2018-07-05 17:02 .spyproject\codest
文件 64 2018-07-05 17:02 .spyproject\encoding.ini
文件 92 2018-07-05 17:02 .spyproject\vcs.ini
文件 415 2018-07-13 17:33 .spyproject\workspace.ini
目录 0 2019-04-25 19:52 __pycache__\
文件 2067 2018-07-09 21:39 __pycache__\json.cpython-35.pyc
目录 0 2018-07-09 17:45 assets\
目录 0 2019-04-25 19:53 images\
文件 13781 2019-04-23 15:50 images\1.jpg
文件 16749 2019-04-23 15:50 images\2.jpg
文件 16132 2019-04-23 15:50 images\3.jpg
文件 12123 2019-04-23 15:50 images\4.jpg
文件 14014 2019-04-23 15:50 images\5.jpg
文件 1095 2018-06-06 14:12 LICENSE
目录 0 2019-04-25 19:55 logs\
文件 58 2018-06-06 14:12 MANIFEST.in
目录 0 2019-04-25 19:52 mrcnn\
文件 1 2018-06-06 14:12 mrcnn\__init__.py
目录 0 2019-04-25 19:52 mrcnn\__pycache__\
文件 140 2018-07-05 17:03 mrcnn\__pycache__\__init__.cpython-35.pyc
文件 123 2019-04-20 10:20 mrcnn\__pycache__\__init__.cpython-36.pyc
文件 3085 2018-07-13 19:59 mrcnn\__pycache__\config.cpython-35.pyc
文件 2841 2019-04-20 10:20 mrcnn\__pycache__\config.cpython-36.pyc
文件 84960 2018-07-05 17:04 mrcnn\__pycache__\model.cpython-35.pyc
文件 77340 2019-04-20 10:20 mrcnn\__pycache__\model.cpython-36.pyc
文件 6268 2018-07-09 18:18 mrcnn\__pycache__\parallel_model.cpython-35.pyc
文件 5791 2019-04-20 19:54 mrcnn\__pycache__\parallel_model.cpython-36.pyc
文件 28954 2018-07-13 15:52 mrcnn\__pycache__\utils.cpython-35.pyc
文件 26689 2019-04-20 10:20 mrcnn\__pycache__\utils.cpython-36.pyc
............此处省略514个文件信息
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