yolov4-custom-functions | Wide Range of Custom Functions | Computer Vision library

by theAIGuysCode Python Version: Current License: MIT

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

kandi X-RAY | yolov4-custom-functions Summary

yolov4-custom-functions is a Python library typically used in Artificial Intelligence, Computer Vision, Tensorflow applications. yolov4-custom-functions 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.

A Wide Range of Custom Functions for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny Implemented in TensorFlow, TFLite, and TensorRT.

Support

Quality

Security

License

Reuse

Support

yolov4-custom-functions has a low active ecosystem.

It has 551 star(s) with 367 fork(s). There are 20 watchers for this library.

It had no major release in the last 6 months.

There are 73 open issues and 17 have been closed. On average issues are closed in 49 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-custom-functions is current.

Quality

yolov4-custom-functions has 0 bugs and 0 code smells.

Security

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

yolov4-custom-functions code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

yolov4-custom-functions 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-custom-functions 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.

yolov4-custom-functions saves you 729 person hours of effort in developing the same functionality from scratch.

It has 1682 lines of code, 61 functions and 13 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed yolov4-custom-functions and discovered the below as its top functions. This is intended to give you an instant insight into yolov4-custom-functions implemented functionality, and help decide if they suit your requirements.

Save the trained model
Return a YOLO instance
YOLOv3 test
YOLOv3
Draw bboxes
Recognize a plate
Reads the class names
Calculate nms using nms
Compute the intersection of two bounding boxes
Load configuration
Get anchors
Resolve a residual block
Convolutional convolutional layer
Saves TrtensorFlow model
Performs OCR
Counts the number of objects in the box
Crop detection from images
Example demo example
Save TFLite

Get all kandi verified functions for this library.

yolov4-custom-functions Key Features

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

yolov4-custom-functions Examples and Code Snippets

No Code Snippets are available at this moment for yolov4-custom-functions.

Community Discussions

Trending Discussions on yolov4-custom-functions

ValueError: Didn't find op for builtin opcode 'RESIZE_BILINEAR' version '3' Registration failed

QUESTION

ValueError: Didn't find op for builtin opcode 'RESIZE_BILINEAR' version '3' Registration failed

Asked 2020-Dec-17 at 08:44

I am using yolov4 tiny on raspberry pi4 with 2GB module.

I install the almost all packages and activate the environment.install tensor-flow and tft-lite but when i execute the detection.py code i get the error.

Value Error: Didn't find op for builtin opcode 'RESIZE_BILINEAR' version '3' Registration failed.

i am using the code of "theAIGuysCode" ...

repo link = https://github.com/theAIGuysCode/yolov4-custom-functions

...

ANSWER

Answered 2020-Dec-17 at 08:44

Problem is mismatch of converter version and runtime version. Make sure you use recommended setup (tf 2.3.0):

Conda (Recommended)
Tensorflow CPU conda env create -f conda-cpu.yml conda activate yolov4-cpu

Tensorflow GPU conda env create -f conda-gpu.yml conda activate yolov4-gpu
Pip
TensorFlow CPU pip install -r requirements.txt

TensorFlow GPU pip install -r requirements-gpu.txt

Check installed version (call print(tf.version) or check requirements.txt ) and install same version for your RPi.

Source https://stackoverflow.com/questions/65287265

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

Vulnerabilities

No vulnerabilities reported

Install yolov4-custom-functions

You can download it from GitHub.
You can use yolov4-custom-functions 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

If there is a custom function you want to see created then create an issue in the issues tab and suggest it! If enough people suggest the same custom function I will add it quickly!.

Find more information at: