ScaledYOLOv4 | fire fire fire | Computer Vision library

by DataXujing Python Version: Current License: No License

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

kandi X-RAY | ScaledYOLOv4 Summary

ScaledYOLOv4 is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch applications. ScaledYOLOv4 has no bugs, it has no vulnerabilities and it has low support. However ScaledYOLOv4 build file is not available. You can download it from GitHub.

ScaledYOLOv4

Support

Quality

Security

License

Reuse

Support

ScaledYOLOv4 has a low active ecosystem.

It has 53 star(s) with 18 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 7 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ScaledYOLOv4 is current.

Quality

ScaledYOLOv4 has 0 bugs and 0 code smells.

Security

ScaledYOLOv4 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

ScaledYOLOv4 code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ScaledYOLOv4 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

ScaledYOLOv4 releases are not available. You will need to build from source code and install.

ScaledYOLOv4 has no build file. You will be need to create the build yourself to build the component from source.

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

It has 3610 lines of code, 210 functions and 21 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed ScaledYOLOv4 and discovered the below as its top functions. This is intended to give you an instant insight into ScaledYOLOv4 implemented functionality, and help decide if they suit your requirements.

Train the model
R Return a list of predicted detections
R Check anchors
Download weights from gdrive
Create a Dataloader
Try to load an ensemble
Fuse all convolution layers
A context manager for tensor
Detects the neural network
Function to apply classification classification
Load a pretrained pretrained model
R Compute a non - greedy prediction
Finds the intersection between two boxes
R Predicate prediction
Parse a model dictionary
Convert an ANTsImage to JPEG
Colorize an image
Print mutation results to evolve
Cache image labels
Performs a single forward computation
Download pretrained weights
Plot hyperparameters in evolution txt file
Forward computation
Forward computation
Tries to reduce images in path
Plot test
Loads an ensemble
Update EMA parameters
Fuse convolution layer
Prune the model

Get all kandi verified functions for this library.

ScaledYOLOv4 Key Features

No Key Features are available at this moment for ScaledYOLOv4.

ScaledYOLOv4 Examples and Code Snippets

No Code Snippets are available at this moment for ScaledYOLOv4.

Community Discussions

Trending Discussions on ScaledYOLOv4

RuntimeError: CUDA out of memory

QUESTION

RuntimeError: CUDA out of memory

Asked 2021-Feb-18 at 10:20

I got this Error:

...

ANSWER

Answered 2021-Feb-17 at 15:40

I finally find it. The problem was, I was using the new CUDA 11.2. That's bad. I remove it. and install CUDA 10.2. That fix the problem.

Source https://stackoverflow.com/questions/66243852

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

Vulnerabilities

No vulnerabilities reported

Install ScaledYOLOv4

You can download it from GitHub.
You can use ScaledYOLOv4 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 .

Find more information at: