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.


  • 0.0.5

Last updated:

  • 14 June 2023

First released:

  • 23 June 2022


Supported data:

  • Information not submitted

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GitHub activity:

  • Stars: 2
  • Forks: 4
  • Issues + PRs: 1

Python versions supported:

Operating system:


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

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