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Detection of apple based on YOLOv4 model

Project description

napari-apple

License BSD-3 PyPI Python Version tests codecov napari hub

Detection of apple based on YOLOv4 model


This napari plugin was generated with Cookiecutter using @napari's cookiecutter-napari-plugin template.

Installation

First, please note that this module only works on a Linux Ubuntu system. Indeed, the launch of the YOLO module is a command that is executed on a Linux Ubuntu system.

Before you can operate the module, you must install the napari-apple module and Darknet on your machine.

Instruction for napari-module

You can install napari-apple via pip:

pip install napari-apple

To install latest development version :

pip install git+https://github.com/hereariim/napari-apple.git

Instruction Darknet

Darknet is the module where the pre-trained YOLO model is located. You can install Darknet by running this command:

git clone https://github.com/pjreddie/darknet
cd darknet
make

When Darknet is installed, you have to put the weights of the apple detection model in the cfg subfolder. You find the weights in the weight-darknet folder.

Contributing

Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.

License

Distributed under the terms of the BSD-3 license, "napari-apple" is free and open source software

Issues

If you encounter any problems, please file an issue along with a detailed description.

Project details


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napari_apple-0.0.6.tar.gz (22.0 MB view hashes)

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Built Distribution

napari_apple-0.0.6-py3-none-any.whl (22.0 MB view hashes)

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