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clevr-dataset-gen | Diagnostic Dataset for Compositional Language | Computer Vision library

 by   facebookresearch Python Version: Current License: Non-SPDX

 by   facebookresearch Python Version: Current License: Non-SPDX

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kandi X-RAY | clevr-dataset-gen Summary

clevr-dataset-gen is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch applications. clevr-dataset-gen has no bugs, it has no vulnerabilities and it has low support. However clevr-dataset-gen build file is not available and it has a Non-SPDX License. You can download it from GitHub.
This is the code used to generate the CLEVR dataset as described in the paper:. CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Fei-Fei Li, Larry Zitnick, Ross Girshick Presented at CVPR 2017.
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Support
Quality
Quality
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License
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kandi-support Support

  • clevr-dataset-gen has a low active ecosystem.
  • It has 388 star(s) with 138 fork(s). There are 15 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 17 open issues and 9 have been closed. On average issues are closed in 30 days. There are 1 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of clevr-dataset-gen is current.
clevr-dataset-gen Support
Best in #Computer Vision
Average in #Computer Vision
clevr-dataset-gen Support
Best in #Computer Vision
Average in #Computer Vision

quality kandi Quality

  • clevr-dataset-gen has 0 bugs and 0 code smells.
clevr-dataset-gen Quality
Best in #Computer Vision
Average in #Computer Vision
clevr-dataset-gen Quality
Best in #Computer Vision
Average in #Computer Vision

securitySecurity

  • clevr-dataset-gen has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • clevr-dataset-gen code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
clevr-dataset-gen Security
Best in #Computer Vision
Average in #Computer Vision
clevr-dataset-gen Security
Best in #Computer Vision
Average in #Computer Vision

license License

  • clevr-dataset-gen has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
clevr-dataset-gen License
Best in #Computer Vision
Average in #Computer Vision
clevr-dataset-gen License
Best in #Computer Vision
Average in #Computer Vision

buildReuse

  • clevr-dataset-gen releases are not available. You will need to build from source code and install.
  • clevr-dataset-gen 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.
  • clevr-dataset-gen saves you 553 person hours of effort in developing the same functionality from scratch.
  • It has 1293 lines of code, 46 functions and 5 files.
  • It has low code complexity. Code complexity directly impacts maintainability of the code.
clevr-dataset-gen Reuse
Best in #Computer Vision
Average in #Computer Vision
clevr-dataset-gen Reuse
Best in #Computer Vision
Average in #Computer Vision
Top functions reviewed by kandi - BETA

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

  • Instantiate templates from a template .
  • Add random objects to a scene .
  • Render a scene .
  • Render a bladeless object .
  • Main function .
  • Add a material to the scene .
  • Insert a scene node into a list .
  • Precompute filter options .
  • Answer the given question .
  • Check if other is in other

clevr-dataset-gen Key Features

A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning

CLEVR Dataset Generation

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@inproceedings{johnson2017clevr,
  title={CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning},
  author={Johnson, Justin and Hariharan, Bharath and van der Maaten, Laurens
          and Fei-Fei, Li and Zitnick, C Lawrence and Girshick, Ross},
  booktitle={CVPR},
  year={2017}
}

Step 1: Generating Images

copy iconCopydownload iconDownload
echo $PWD/image_generation >> $BLENDER/$VERSION/python/lib/python3.5/site-packages/clevr.pth

Step 2: Generating Questions

copy iconCopydownload iconDownload
cd question_generation
python generate_questions.py

Community Discussions

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QUESTION

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!)

ANSWER

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.

1-(distance/Float.greatestFiniteMagnitude)*100

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

Vulnerabilities

No vulnerabilities reported

Install clevr-dataset-gen

You can download it from GitHub.
You can use clevr-dataset-gen 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

Next we generate questions, functional programs, and answers for the rendered images generated in the previous step. This step takes as input the single JSON file containing all ground-truth scene information, and outputs a JSON file containing questions, answers, and functional programs for the questions in a single JSON file.

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