PyTorch_YOLOv4 | PyTorch implementation of YOLOv4 | Machine Learning library

by WongKinYiu Python Version: Current License: No License

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | PyTorch_YOLOv4 Summary

PyTorch_YOLOv4 is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. PyTorch_YOLOv4 has no bugs, it has no vulnerabilities, it has build file available and it has medium support. You can download it from GitHub.

PyTorch implementation of YOLOv4

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Quality

Security

License

Reuse

Support

PyTorch_YOLOv4 has a medium active ecosystem.

It has 1793 star(s) with 580 fork(s). There are 32 watchers for this library.

It had no major release in the last 6 months.

There are 302 open issues and 120 have been closed. On average issues are closed in 62 days. There are 8 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of PyTorch_YOLOv4 is current.

Quality

PyTorch_YOLOv4 has 0 bugs and 0 code smells.

Security

PyTorch_YOLOv4 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

PyTorch_YOLOv4 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

PyTorch_YOLOv4 does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

PyTorch_YOLOv4 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.

PyTorch_YOLOv4 saves you 1957 person hours of effort in developing the same functionality from scratch.

It has 4548 lines of code, 264 functions and 19 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed PyTorch_YOLOv4 and discovered the below as its top functions. This is intended to give you an instant insight into PyTorch_YOLOv4 implemented functionality, and help decide if they suit your requirements.

Train a trained model .
Construct module blocks from module definitions .
Run darknet detection .
k - mean anchor anchors
Returns a randomly selected image .
Random perspective of image .
Runs a prediction on a prediction .
Loads all mosaic mosaic images .
Performs a single step .
Compute the loss for a given model .

Get all kandi verified functions for this library.

PyTorch_YOLOv4 Key Features

No Key Features are available at this moment for PyTorch_YOLOv4.

PyTorch_YOLOv4 Examples and Code Snippets

README,Excute:

C++

Lines of Code : 20

License : Permissive (MIT)

Copy

  git clone -b u3_preview https://github.com/WongKinYiu/PyTorch_YOLOv4.git

  git clone https://github.com/tjuskyzhang/yolov4-tiny-tensorrt.git

  cd PyTorch_YOLOv4

  cp ../yolov4-tiny-tensorrt/gen_wts.py .

  python gen_wts.py weights/yolov4-tiny.p

Pytorch-YOLOv4,test-dev

Python

Lines of Code : 12

License : No License

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Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.411
Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.639
Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.444
Average Preci

Pytorch-FCOS,test-dev

Python

Lines of Code : 1

License : No License

Copy

Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.xxx

Community Discussions

Trending Discussions on PyTorch_YOLOv4

Unsupported ONNX opset version: 11

QUESTION

Unsupported ONNX opset version: 11

Asked 2020-Nov-18 at 05:43

I'm following this guide to convert darknet to onnx. However, I'm facing the following error:

"C:\Users\Scott\Anaconda3\envs\pytorch_yolov4\lib\site-packages\torch\onnx\symbolic_helper.py", line 253, in _set_opset_version raise ValueError("Unsupported ONNX opset version: " + str(opset_version)) ValueError: Unsupported ONNX opset version: 11

What does this error mean and how to deal with it?

...

ANSWER

Answered 2020-Nov-18 at 05:43

It looks like you have an old PyTorch version, probably PyTorch 1.2.

The docs here https://github.com/Tianxiaomo/pytorch-YOLOv4#4-pytorch2onnx recommend at least PyTorch 1.4.

Source https://stackoverflow.com/questions/64886507

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

Vulnerabilities

No vulnerabilities reported

Install PyTorch_YOLOv4

You can download it from GitHub.
You can use PyTorch_YOLOv4 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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