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42 lines (39 loc) · 1.63 KB
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from setuptools import setup, find_packages
with open("README.md", "r", encoding="utf-8") as fh:
long_description = fh.read()
setup(
name="litecnn",
version="1.0.3",
description="LiteCNN: Intuitive Python library for creating, training and visualizing convolutional neural networks. Features simplified CNN layer definition, automated training workflows, model visualization, and seamless Keras-to-ONNX conversion. Includes 15 pre-configured popular models for immediate use.",
long_description=long_description,
long_description_content_type="text/markdown",
project_urls={
"Documentation": "https://github.com/Gabrli/EasyCNN---docs",
"Source Code" :"https://github.com/Gabrli/easyCNN",
},
keywords="deep-learning, cnn, neural-networks, tensorflow, keras, machine-learning, ai, computer-vision, image-processing, model-training, visualization, easy-to-use, python, convolutional-networks",
packages=find_packages(),
author="Gabriel Wiśniewski",
author_email="gabrys.wisniewski@op.pl",
classifiers=[
"Programming Language :: Python :: 3",
"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
],
install_requires=[
"tensorflow",
"numpy",
"matplotlib",
"opencv-python",
"tf2onnx",
"onnx"
],
entry_points={
"console_scripts":[
"litecnn-core = litecnn:LiteCNN",
"litecnn-preset = litecnn:Preset",
"litecnn-visualizer = litecnn:TrainingVisualizer",
"litecnn-exporter = litecnn:EasyExporter",
]
}
)