yolov3-keras-tf2 | yolo implementation in keras and tensorflow | Machine Learning library

by emadboctorx Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | yolov3-keras-tf2 Summary

yolov3-keras-tf2 is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Keras applications. yolov3-keras-tf2 has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

yolo(v3/v4) implementation in keras and tensorflow 2.2

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Quality

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yolov3-keras-tf2 has a low active ecosystem.

It has 611 star(s) with 111 fork(s). There are 32 watchers for this library.

It had no major release in the last 6 months.

There are 4 open issues and 5 have been closed. On average issues are closed in 2 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of yolov3-keras-tf2 is current.

Quality

yolov3-keras-tf2 has 0 bugs and 0 code smells.

Security

yolov3-keras-tf2 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

yolov3-keras-tf2 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

yolov3-keras-tf2 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

yolov3-keras-tf2 releases are not available. You will need to build from source code and install.

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

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

yolov3-keras-tf2 saves you 1842 person hours of effort in developing the same functionality from scratch.

It has 1434 lines of code, 66 functions and 15 files.

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

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yolov3-keras-tf2 Key Features

No Key Features are available at this moment for yolov3-keras-tf2.

yolov3-keras-tf2 Examples and Code Snippets

No Code Snippets are available at this moment for yolov3-keras-tf2.

Community Discussions

Trending Discussions on yolov3-keras-tf2

Shape mismatch problem in tensorflow 2.2 training using yolo4.cfg

QUESTION

Shape mismatch problem in tensorflow 2.2 training using yolo4.cfg

Asked 2020-Jun-02 at 12:45

I recently added a new feature to my yolov3 implementation which is models are currently loaded directly from DarkNet cfg files for convenience, I tested the code with yolov3 configuration as well as yolov4 configuration they both work just fine except for v4 training. Shortly after I start training I get a shapes mismatch error and I'll be very grateful if someone can help me get rid of the error and get to finally complete my project. Please let me know in the comments and I will provide you with any resources you need to help me with fixing the problem and thank you in advance...

This is what I run in order to reproduce:

...

ANSWER

Answered 2020-Jun-02 at 12:45

Adding this line in models.py solved the shapes problem and the training started as expected:

Source https://stackoverflow.com/questions/62102912

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

Vulnerabilities

No vulnerabilities reported

Install yolov3-keras-tf2

You can download it from GitHub.
You can use yolov3-keras-tf2 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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