pytorch-YOLOv4 | PyTorch , ONNX and TensorRT implementation of YOLOv4 | Computer Vision library

by Tianxiaomo Python Version: Current License: Apache-2.0

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 Telecommunications, Media, Media, Entertainment, Artificial Intelligence, Computer Vision, Pytorch applications. pytorch-YOLOv4 has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. However pytorch-YOLOv4 has 6 bugs. You can download it from GitHub.

PyTorch ,ONNX and TensorRT implementation of YOLOv4

Support

Quality

Security

License

Reuse

Support

pytorch-YOLOv4 has a medium active ecosystem.

It has 4271 star(s) with 1471 fork(s). There are 55 watchers for this library.

It had no major release in the last 6 months.

There are 327 open issues and 168 have been closed. On average issues are closed in 24 days. There are 13 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 6 bugs (4 blocker, 0 critical, 2 major, 0 minor) and 182 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 16 security hotspots that need review.

License

pytorch-YOLOv4 is licensed under the Apache-2.0 License. This license is Permissive.

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

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 2140 person hours of effort in developing the same functionality from scratch.

It has 4690 lines of code, 224 functions and 30 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 the model
Evaluate a model
Summarize the IOC evaluation
Synchronize the coo_eval
Detect the cv2 camera
Print configuration of blocks
Load weights from a weightfile
Forward computation
Builds the target tensorflow matrices
Compute the bounding box between two boxes
Generate a test graph
Plot boxes using cv2
Perform forward computation
Extracts boxes and confs
Forward computation
Get command line arguments
Run inference on a given image
Runs an image detection
Detects the cv2 image
Forward loss function
Train one epoch
Parse config file
Transform weights to onnx
Plot boxes of boxes
Print out the configuration
Runs the test on the model
Yolo - learn model
Evaluate the given model
Load weights from a weight file

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

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-

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-YOLOv4,训练自定义数据集

Python

Lines of Code : 3

License : No License

Copy

xxx.jpg 18.19,6.32,424.13,421.83,20 323.86,2.65,640.0,421.94,20
xxx.jpg 48,240,195,371,11 8,12,352,498,14
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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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