yolov4-pytorch | This is a YoloV4-pytorch source code that can be

by bubbliiiing Python Version: v3.1 License: MIT

X-Ray Key Features Code Snippets(3)Community Discussions Vulnerabilities Install Support

kandi X-RAY | yolov4-pytorch Summary

yolov4-pytorch is a Python library. yolov4-pytorch has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

This is a YoloV4-pytorch source code that can be used to train your own model.

Support

Quality

Security

License

Reuse

Support

yolov4-pytorch has a medium active ecosystem.

It has 1891 star(s) with 597 fork(s). There are 13 watchers for this library.

It had no major release in the last 12 months.

There are 279 open issues and 78 have been closed. On average issues are closed in 42 days. There are 6 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of yolov4-pytorch is v3.1

Quality

yolov4-pytorch has 0 bugs and 63 code smells.

Security

yolov4-pytorch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

yolov4-pytorch code analysis shows 0 unresolved vulnerabilities.

There are 2 security hotspots that need review.

License

yolov4-pytorch is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

yolov4-pytorch releases are available to install and integrate.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

yolov4-pytorch saves you 1090 person hours of effort in developing the same functionality from scratch.

It has 2467 lines of code, 79 functions and 18 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

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yolov4-pytorch Key Features

No Key Features are available at this moment for yolov4-pytorch.

yolov4-pytorch Examples and Code Snippets

Installation

Python

Lines of Code : 102

License : Permissive (MIT)

Copy

conda install numpy ninja pyyaml mkl mkl-include setuptools cmake cffi typing_extensions future six requests dataclasses

# Add LAPACK support for the GPU if needed
conda install -c pytorch magma-cuda102  # or [ magma-cuda101 | magma-cuda100 | magma-

Monitoring COVID-19 prevention measures on CCTV cameras using Deep Learning,Usage,detector

Python

Lines of Code : 14

License : Strong Copyleft (GPL-3.0)

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${COVID_MONITOR}
|-- detector
|   |-- __init__.py
|   |-- detector.py
|   |   |-- yolov4

class Detector(ABC):
    @abstractmethod
    def detect(self, image, targets='None'):
        """
                :param image: image to perform the inference o

ovml,Installation

Lines of Code : 2

License : Permissive (MIT)

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## install.packages("remotes")
remotes::install_github("openvolley/ovml")

Community Discussions

No Community Discussions are available at this moment for yolov4-pytorch.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install yolov4-pytorch

You can download it from GitHub.
You can use yolov4-pytorch like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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