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Object-Detection | object detection using SSD Mobile Net v3 | Computer Vision library

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kandi X-RAY | Object-Detection Summary

Object-Detection is a Python library typically used in Artificial Intelligence, Computer Vision, OpenCV, Xamarin applications. Object-Detection has no bugs, it has no vulnerabilities and it has low support. However Object-Detection build file is not available. You can download it from GitHub.
object detection using SSD Mobile Net v3

kandi-support Support

  • Object-Detection has a low active ecosystem.
  • It has 4 star(s) with 3 fork(s). There are 1 watchers for this library.
  • It had no major release in the last 12 months.
  • Object-Detection has no issues reported. There are no pull requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of Object-Detection is current.

quality kandi Quality

  • Object-Detection has no bugs reported.


  • Object-Detection has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

license License

  • Object-Detection 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.


  • Object-Detection releases are not available. You will need to build from source code and install.
  • Object-Detection has no build file. You will be need to create the build yourself to build the component from source.
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Object-Detection Key Features

object detection using SSD Mobile Net v3

Object-Detection Examples and Code Snippets

No Code Snippets are available at this moment for Object-Detection.Refer to component home page for details.

No Code Snippets are available at this moment for Object-Detection.Refer to component home page for details.

Community Discussions

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Trending Discussions on Computer Vision


Image similarity in swift

Asked 2022-Mar-25 at 11:42

The swift vision similarity feature is able to assign a number to the variance between 2 images. Where 0 variance between the images, means the images are the same. As the number increases this that there is more and more variance between the images.

What I am trying to do is turn this into a percentage of similarity. So one image is for example 80% similar to the other image. Any ideas how I could arrange the logic to accomplish this:

import UIKit
import Vision
func featureprintObservationForImage(atURL url: URL) -> VNFeaturePrintObservation? {
let requestHandler = VNImageRequestHandler(url: url, options: [:])
let request = VNGenerateImageFeaturePrintRequest()
do {
  try requestHandler.perform([request])
  return request.results?.first as? VNFeaturePrintObservation
} catch {
  print("Vision error: \(error)")
  return nil
 let apple1 = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"apple1", withExtension: "jpg")!)
let apple2 = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"apple2", withExtension: "jpg")!)
let pear = featureprintObservationForImage(atURL: Bundle.main.url(forResource:"pear", withExtension: "jpg")!)
var distance = Float(0)
try apple1!.computeDistance(&distance, to: apple2!)
var distance2 = Float(0)
try apple1!.computeDistance(&distance2, to: pear!)


Answered 2022-Mar-25 at 10:26

It depends on how you want to scale it. If you just want the percentage you could just use Float.greatestFiniteMagnitude as the maximum value.


A better solution would probably be to set a lower ceiling and everything above that ceiling would just be 0% similarity.

1-(min(distance, 10)/10)*100

Here the artificial ceiling would be 10, but it can be any arbitrary number.

Source https://stackoverflow.com/questions/71615277

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


No vulnerabilities reported

Install Object-Detection

You can download it from GitHub.
You can use Object-Detection 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.


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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