Detection of apple based on YOLOv4 model

    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.


    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+

    Instruction Darknet

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

    git clone
    cd darknet

    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.


    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.


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


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


    • 0.0.2

    Last updated:

    • 08 December 2022

    First released:

    • 23 June 2022


    • BSD-3-Clause

    Supported data:

    • Information not submitted

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    GitHub activity:

    • Stars: 3
    • Forks: 0
    • Issues + PRs: 0

    Python versions supported:

    Operating system:


    • numpy
    • magicgui
    • qtpy
    • opencv-python-headless
    • scikit-image
    • napari

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